library(renv)
library(tidyverse)
library(broom)
library(labelled)
library(psych)
library(GPArotation)
#library(devtools)
#install_github("cran/multicon") # not on CRAN atm
library(multicon)
library(correlation)
library(careless)
library(corrplot)
library(lavaan)
library(semTools)
library(semPlot)
library(knitr)
library(ggdist)
library(ggforce)
library(cowplot)
library(nortest)
library(lmerTest)
library(wordcloud) # these 3 for the word clouds
library(RColorBrewer)
library(tm)source("clean_data_st1.R")Reshape data
df_sbsa_wide_wb <- df_sbsa %>%
arrange(pid, time) %>%
select(pid, time, rando, starts_with(c("sw06", "ml01", "rs01", "sc01"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = starts_with(c("sw06", "ml01", "rs01", "sc01")))
# colnames(df_sbsa_wide_wb)Check CFA item loadings at T1
cfa_meaning <- '
# Define the latent factors
meaning1 =~ NA*ml01_01_t1 + lambda1*ml01_01_t1 + lambda2*ml01_02_t1 + lambda3*ml01_03_t1 + lambda4*ml01_04_t1 + lambda5*ml01_05_t1 + lambda6*ml01_06_t1 + lambda7*ml01_07_t1 + lambda8*ml01_08_t1 + lambda9*ml01_09_t1 + lambda10*ml01_10_t1
# Intercepts
ml01_01_t1 ~ i1*1
ml01_02_t1 ~ 1
ml01_03_t1 ~ 1
ml01_04_t1 ~ 1
ml01_05_t1 ~ 1
ml01_06_t1 ~ 1
ml01_07_t1 ~ 1
ml01_08_t1 ~ 1
ml01_09_t1 ~ 1
ml01_10_t1 ~ 1
# Unique Variances
ml01_01_t1 ~~ ml01_01_t1
ml01_02_t1 ~~ ml01_02_t1
ml01_03_t1 ~~ ml01_03_t1
ml01_04_t1 ~~ ml01_04_t1
ml01_05_t1 ~~ ml01_05_t1
ml01_06_t1 ~~ ml01_06_t1
ml01_07_t1 ~~ ml01_07_t1
ml01_08_t1 ~~ ml01_08_t1
ml01_09_t1 ~~ ml01_09_t1
ml01_10_t1 ~~ ml01_10_t1
# Latent Variable Means
meaning1 ~ 0*1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
'
fit_cfa_meaning <- cfa(cfa_meaning, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_cfa_meaning, fit.measures = TRUE)lavaan 0.6.15 ended normally after 36 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Used Total
Number of observations 697 715
Number of missing patterns 2
Model Test User Model:
Test statistic 2033.788
Degrees of freedom 35
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4515.632
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.553
Tucker-Lewis Index (TLI) 0.425
Robust Comparative Fit Index (CFI) 0.553
Robust Tucker-Lewis Index (TLI) 0.425
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12063.842
Loglikelihood unrestricted model (H1) -11046.948
Akaike (AIC) 24187.684
Bayesian (BIC) 24324.088
Sample-size adjusted Bayesian (SABIC) 24228.832
Root Mean Square Error of Approximation:
RMSEA 0.286
90 Percent confidence interval - lower 0.276
90 Percent confidence interval - upper 0.297
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.286
90 Percent confidence interval - lower 0.276
90 Percent confidence interval - upper 0.297
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.229
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
meaning1 =~
m01_01_ (lmb1) 1.355 0.053 25.543 0.000
m01_02_ (lmb2) -0.392 0.057 -6.855 0.000
m01_03_ (lmb3) -0.224 0.062 -3.631 0.000
m01_04_ (lmb4) 1.513 0.052 28.862 0.000
m01_05_ (lmb5) 1.340 0.053 25.155 0.000
m01_06_ (lmb6) 1.440 0.054 26.620 0.000
m01_07_ (lmb7) -0.199 0.059 -3.396 0.001
m01_08_ (lmb8) -0.346 0.063 -5.490 0.000
m01_09_ (lmb9) -1.440 0.062 -23.048 0.000
m01_10_ (lm10) -0.720 0.062 -11.555 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.ml01_01_1 (i1) 4.175 0.063 66.262 0.000
.ml01_02_1 5.365 0.055 97.095 0.000
.ml01_03_1 5.033 0.059 85.888 0.000
.ml01_04_1 3.857 0.065 59.286 0.000
.ml01_05_1 4.387 0.063 69.727 0.000
.ml01_06_1 3.891 0.065 59.699 0.000
.ml01_07_1 5.026 0.056 90.101 0.000
.ml01_08_1 4.984 0.060 82.648 0.000
.ml01_09_1 3.898 0.072 54.299 0.000
.ml01_10_1 4.927 0.062 79.130 0.000
meaning1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.ml01_01_t1 0.931 0.061 15.370 0.000
.ml01_02_t1 1.972 0.107 18.491 0.000
.ml01_03_t1 2.343 0.126 18.620 0.000
.ml01_04_t1 0.660 0.052 12.713 0.000
.ml01_05_t1 0.964 0.062 15.528 0.000
.ml01_06_t1 0.888 0.060 14.738 0.000
.ml01_07_t1 2.129 0.114 18.628 0.000
.ml01_08_t1 2.415 0.130 18.554 0.000
.ml01_09_t1 1.518 0.093 16.374 0.000
.ml01_10_t1 2.184 0.120 18.155 0.000
meaning1 1.000 tidy(fit_cfa_meaning) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 10 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 meaning1 =~ ml01_04_t1 =~ lambda4 1.51 0.0524 0.881
2 meaning1 =~ ml01_09_t1 =~ lambda9 -1.44 0.0625 -0.760
3 meaning1 =~ ml01_06_t1 =~ lambda6 1.44 0.0541 0.837
4 meaning1 =~ ml01_01_t1 =~ lambda1 1.35 0.0530 0.815
5 meaning1 =~ ml01_05_t1 =~ lambda5 1.34 0.0533 0.807
6 meaning1 =~ ml01_10_t1 =~ lambda10 -0.720 0.0623 -0.438
7 meaning1 =~ ml01_02_t1 =~ lambda2 -0.392 0.0571 -0.269
8 meaning1 =~ ml01_08_t1 =~ lambda8 -0.346 0.0631 -0.218
9 meaning1 =~ ml01_03_t1 =~ lambda3 -0.224 0.0617 -0.145
10 meaning1 =~ ml01_07_t1 =~ lambda7 -0.199 0.0587 -0.135Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_wb <- df_sbsa_wide_wb %>%
mutate(ml01_09_t1_r = ml01_09_t1,
ml01_10_t1_r = ml01_10_t1,
ml01_02_t1_r = ml01_02_t1,
ml01_08_t1_r = ml01_08_t1,
ml01_07_t1_r = ml01_07_t1,
ml01_03_t1_r = ml01_03_t1,
ml01_09_t2_r = ml01_09_t2,
ml01_10_t2_r = ml01_10_t2,
ml01_02_t2_r = ml01_02_t2,
ml01_08_t2_r = ml01_08_t2,
ml01_07_t2_r = ml01_07_t2,
ml01_03_t2_r = ml01_03_t2) %>%
mutate(across(intersect(starts_with("ml01"), ends_with("_r")),
~ recode(.x, `1` = 7L, `2` = 6L, `3` = 5L, `4` = 4L, `5` = 3L, `6` = 2L, `7` = 1L, .default = NA_integer_))) %>%
mutate(meaning_par1_t1 = rowMeans(across(c(ml01_04_t1, ml01_10_t1_r, ml01_03_t1_r, ml01_07_t1_r)), na.rm=T),
meaning_par2_t1 = rowMeans(across(c(ml01_09_t1_r, ml01_05_t1, ml01_08_t1_r)), na.rm=T),
meaning_par3_t1 = rowMeans(across(c(ml01_06_t1, ml01_01_t1, ml01_02_t1_r)), na.rm=T),
meaning_par1_t2 = rowMeans(across(c(ml01_04_t2, ml01_10_t2_r, ml01_03_t2_r, ml01_07_t2_r)), na.rm=T),
meaning_par2_t2 = rowMeans(across(c(ml01_09_t2_r, ml01_05_t2, ml01_08_t2_r)), na.rm=T),
meaning_par3_t2 = rowMeans(across(c(ml01_06_t2, ml01_01_t2, ml01_02_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_selfes <- '
# Define the latent factors
selfes1 =~ NA*rs01_01_t1 + lambda1*rs01_01_t1 + lambda2*rs01_02_t1 + lambda3*rs01_03_t1 + lambda4*rs01_04_t1 + lambda5*rs01_05_t1 + lambda6*rs01_06_t1 + lambda7*rs01_07_t1 + lambda8*rs01_08_t1 + lambda9*rs01_09_t1 + lambda10*rs01_10_t1
# Intercepts
rs01_01_t1 ~ i1*1
rs01_02_t1 ~ 1
rs01_03_t1 ~ 1
rs01_04_t1 ~ 1
rs01_05_t1 ~ 1
rs01_06_t1 ~ 1
rs01_07_t1 ~ 1
rs01_08_t1 ~ 1
rs01_09_t1 ~ 1
rs01_10_t1 ~ 1
# Unique Variances
rs01_01_t1 ~~ rs01_01_t1
rs01_02_t1 ~~ rs01_02_t1
rs01_03_t1 ~~ rs01_03_t1
rs01_04_t1 ~~ rs01_04_t1
rs01_05_t1 ~~ rs01_05_t1
rs01_06_t1 ~~ rs01_06_t1
rs01_07_t1 ~~ rs01_07_t1
rs01_08_t1 ~~ rs01_08_t1
rs01_09_t1 ~~ rs01_09_t1
rs01_10_t1 ~~ rs01_10_t1
# Latent Variable Means
selfes1 ~ 0*1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
'
fit_cfa_selfes <- cfa(cfa_selfes, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_cfa_selfes, fit.measures = TRUE)lavaan 0.6.15 ended normally after 14 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Used Total
Number of observations 697 715
Number of missing patterns 1
Model Test User Model:
Test statistic 503.552
Degrees of freedom 35
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3492.047
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.864
Tucker-Lewis Index (TLI) 0.825
Robust Comparative Fit Index (CFI) 0.864
Robust Tucker-Lewis Index (TLI) 0.825
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -9587.382
Loglikelihood unrestricted model (H1) -9335.606
Akaike (AIC) 19234.764
Bayesian (BIC) 19371.168
Sample-size adjusted Bayesian (SABIC) 19275.912
Root Mean Square Error of Approximation:
RMSEA 0.139
90 Percent confidence interval - lower 0.128
90 Percent confidence interval - upper 0.149
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.139
90 Percent confidence interval - lower 0.128
90 Percent confidence interval - upper 0.149
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.066
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
selfes1 =~
r01_01_ (lmb1) 0.881 0.041 21.562 0.000
r01_02_ (lmb2) -0.941 0.046 -20.624 0.000
r01_03_ (lmb3) 0.516 0.033 15.679 0.000
r01_04_ (lmb4) 0.619 0.036 17.043 0.000
r01_05_ (lmb5) -0.875 0.045 -19.595 0.000
r01_06_ (lmb6) -1.033 0.045 -23.146 0.000
r01_07_ (lmb7) 0.674 0.038 17.791 0.000
r01_08_ (lmb8) -0.554 0.046 -12.021 0.000
r01_09_ (lmb9) -1.086 0.043 -25.135 0.000
r01_10_ (lm10) 0.995 0.039 25.685 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.rs01_01_1 (i1) 2.947 0.046 64.218 0.000
.rs01_02_1 3.340 0.050 66.153 0.000
.rs01_03_1 3.947 0.034 115.122 0.000
.rs01_04_1 3.789 0.038 98.424 0.000
.rs01_05_1 2.898 0.049 59.213 0.000
.rs01_06_1 3.323 0.051 65.133 0.000
.rs01_07_1 3.627 0.040 89.658 0.000
.rs01_08_1 3.854 0.047 82.849 0.000
.rs01_09_1 2.692 0.051 52.878 0.000
.rs01_10_1 3.209 0.046 69.858 0.000
selfes1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.rs01_01_t1 0.692 0.042 16.574 0.000
.rs01_02_t1 0.890 0.054 16.595 0.000
.rs01_03_t1 0.553 0.031 17.626 0.000
.rs01_04_t1 0.649 0.037 17.455 0.000
.rs01_05_t1 0.904 0.053 17.076 0.000
.rs01_06_t1 0.746 0.048 15.672 0.000
.rs01_07_t1 0.687 0.040 17.291 0.000
.rs01_08_t1 1.201 0.066 18.189 0.000
.rs01_09_t1 0.626 0.041 15.162 0.000
.rs01_10_t1 0.481 0.033 14.759 0.000
selfes1 1.000 tidy(fit_cfa_selfes) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 10 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 selfes1 =~ rs01_09_t1 =~ lambda9 -1.09 0.0432 -0.808
2 selfes1 =~ rs01_06_t1 =~ lambda6 -1.03 0.0446 -0.767
3 selfes1 =~ rs01_10_t1 =~ lambda10 0.995 0.0387 0.820
4 selfes1 =~ rs01_02_t1 =~ lambda2 -0.941 0.0456 -0.706
5 selfes1 =~ rs01_01_t1 =~ lambda1 0.881 0.0408 0.727
6 selfes1 =~ rs01_05_t1 =~ lambda5 -0.875 0.0447 -0.677
7 selfes1 =~ rs01_07_t1 =~ lambda7 0.674 0.0379 0.631
8 selfes1 =~ rs01_04_t1 =~ lambda4 0.619 0.0363 0.609
9 selfes1 =~ rs01_08_t1 =~ lambda8 -0.554 0.0461 -0.451
10 selfes1 =~ rs01_03_t1 =~ lambda3 0.516 0.0329 0.570Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_wb <- df_sbsa_wide_wb %>%
mutate(rs01_09_t1_r = rs01_09_t1,
rs01_06_t1_r = rs01_06_t1,
rs01_02_t1_r = rs01_02_t1,
rs01_05_t1_r = rs01_05_t1,
rs01_08_t1_r = rs01_08_t1,
rs01_09_t2_r = rs01_09_t2,
rs01_06_t2_r = rs01_06_t2,
rs01_02_t2_r = rs01_02_t2,
rs01_05_t2_r = rs01_05_t2,
rs01_08_t2_r = rs01_08_t2) %>%
mutate(across(intersect(starts_with("rs01"), ends_with("_r")),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(selfes_par1_t1 = rowMeans(across(c(rs01_09_t1_r, rs01_05_t1_r, rs01_08_t1_r, rs01_03_t1)), na.rm=T),
selfes_par2_t1 = rowMeans(across(c(rs01_06_t1_r, rs01_01_t1, rs01_04_t1)), na.rm=T),
selfes_par3_t1 = rowMeans(across(c(rs01_10_t1, rs01_02_t1_r, rs01_07_t1)), na.rm=T),
selfes_par1_t2 = rowMeans(across(c(rs01_09_t2_r, rs01_05_t2_r, rs01_08_t2_r, rs01_03_t2)), na.rm=T),
selfes_par2_t2 = rowMeans(across(c(rs01_06_t2_r, rs01_01_t2, rs01_04_t2)), na.rm=T),
selfes_par3_t2 = rowMeans(across(c(rs01_10_t2, rs01_02_t2_r, rs01_07_t2)), na.rm=T))Check CFA item loadings at T1
cfa_concept <- '
# Define the latent factors
concept1 =~ NA*sc01_01_t1 + lambda1*sc01_01_t1 + lambda2*sc01_02_t1 + lambda3*sc01_03_t1 + lambda4*sc01_04_t1 + lambda5*sc01_05_t1 + lambda6*sc01_06_t1 + lambda7*sc01_07_t1 + lambda8*sc01_08_t1 + lambda9*sc01_09_t1 + lambda10*sc01_10_t1 + lambda11*sc01_11_t1 + lambda12*sc01_12_t1
# Intercepts
sc01_01_t1 ~ i1*1
sc01_02_t1 ~ 1
sc01_03_t1 ~ 1
sc01_04_t1 ~ 1
sc01_05_t1 ~ 1
sc01_06_t1 ~ 1
sc01_07_t1 ~ 1
sc01_08_t1 ~ 1
sc01_09_t1 ~ 1
sc01_10_t1 ~ 1
sc01_11_t1 ~ 1
sc01_12_t1 ~ 1
# Unique Variances
sc01_01_t1 ~~ sc01_01_t1
sc01_02_t1 ~~ sc01_02_t1
sc01_03_t1 ~~ sc01_03_t1
sc01_04_t1 ~~ sc01_04_t1
sc01_05_t1 ~~ sc01_05_t1
sc01_06_t1 ~~ sc01_06_t1
sc01_07_t1 ~~ sc01_07_t1
sc01_08_t1 ~~ sc01_08_t1
sc01_09_t1 ~~ sc01_09_t1
sc01_10_t1 ~~ sc01_10_t1
sc01_11_t1 ~~ sc01_11_t1
sc01_12_t1 ~~ sc01_12_t1
# Latent Variable Means
concept1 ~ 0*1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
'
fit_cfa_concept <- cfa(cfa_concept, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_cfa_concept, fit.measures = TRUE)lavaan 0.6.15 ended normally after 37 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 697 715
Number of missing patterns 2
Model Test User Model:
Test statistic 399.384
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3266.666
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.892
Tucker-Lewis Index (TLI) 0.868
Robust Comparative Fit Index (CFI) 0.892
Robust Tucker-Lewis Index (TLI) 0.868
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12476.420
Loglikelihood unrestricted model (H1) -12276.727
Akaike (AIC) 25024.839
Bayesian (BIC) 25188.523
Sample-size adjusted Bayesian (SABIC) 25074.217
Root Mean Square Error of Approximation:
RMSEA 0.096
90 Percent confidence interval - lower 0.087
90 Percent confidence interval - upper 0.105
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.998
Robust RMSEA 0.096
90 Percent confidence interval - lower 0.087
90 Percent confidence interval - upper 0.105
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.999
Standardized Root Mean Square Residual:
SRMR 0.048
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
concept1 =~
s01_01_ (lmb1) 0.817 0.043 18.924 0.000
s01_02_ (lmb2) 0.949 0.045 21.141 0.000
s01_03_ (lmb3) 0.751 0.046 16.392 0.000
s01_04_ (lmb4) 0.851 0.045 18.729 0.000
s01_05_ (lmb5) 0.751 0.048 15.623 0.000
s01_06_ (lmb6) 0.052 0.047 1.104 0.270
s01_07_ (lmb7) 0.741 0.049 15.064 0.000
s01_08_ (lmb8) 0.996 0.042 23.772 0.000
s01_09_ (lmb9) 1.038 0.044 23.459 0.000
s01_10_ (lm10) 0.850 0.047 18.256 0.000
s01_11_ (lm11) -0.687 0.042 -16.443 0.000
s01_12_ (lm12) 0.828 0.046 17.918 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.sc01_01_1 (i1) 3.232 0.047 69.254 0.000
.sc01_02_1 3.485 0.050 69.942 0.000
.sc01_03_1 3.354 0.048 69.815 0.000
.sc01_04_1 3.301 0.049 67.480 0.000
.sc01_05_1 2.981 0.050 59.621 0.000
.sc01_06_1 2.927 0.045 65.525 0.000
.sc01_07_1 2.780 0.051 54.610 0.000
.sc01_08_1 2.772 0.048 57.506 0.000
.sc01_09_1 2.852 0.051 56.279 0.000
.sc01_10_1 2.941 0.050 59.017 0.000
.sc01_11_1 3.224 0.044 73.560 0.000
.sc01_12_1 3.116 0.049 63.125 0.000
concept1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.sc01_01_t1 0.851 0.050 16.949 0.000
.sc01_02_t1 0.831 0.051 16.250 0.000
.sc01_03_t1 1.045 0.060 17.490 0.000
.sc01_04_t1 0.945 0.056 16.952 0.000
.sc01_05_t1 1.179 0.067 17.620 0.000
.sc01_06_t1 1.388 0.074 18.664 0.000
.sc01_07_t1 1.258 0.071 17.756 0.000
.sc01_08_t1 0.627 0.041 15.179 0.000
.sc01_09_t1 0.713 0.046 15.377 0.000
.sc01_10_t1 1.009 0.059 17.051 0.000
.sc01_11_t1 0.867 0.050 17.481 0.000
.sc01_12_t1 1.012 0.059 17.197 0.000
concept1 1.000 tidy(fit_cfa_concept) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 concept1 =~ sc01_09_t1 =~ lambda9 1.04 0.0442 0.776
2 concept1 =~ sc01_08_t1 =~ lambda8 0.996 0.0419 0.783
3 concept1 =~ sc01_02_t1 =~ lambda2 0.949 0.0449 0.721
4 concept1 =~ sc01_04_t1 =~ lambda4 0.851 0.0454 0.659
5 concept1 =~ sc01_10_t1 =~ lambda10 0.850 0.0465 0.646
6 concept1 =~ sc01_12_t1 =~ lambda12 0.828 0.0462 0.635
7 concept1 =~ sc01_01_t1 =~ lambda1 0.817 0.0432 0.663
8 concept1 =~ sc01_05_t1 =~ lambda5 0.751 0.0481 0.569
9 concept1 =~ sc01_03_t1 =~ lambda3 0.751 0.0458 0.592
10 concept1 =~ sc01_07_t1 =~ lambda7 0.741 0.0492 0.551
11 concept1 =~ sc01_11_t1 =~ lambda11 -0.687 0.0418 -0.593
12 concept1 =~ sc01_06_t1 =~ lambda6 0.0520 0.0471 0.0441Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_wb <- df_sbsa_wide_wb %>% # based on the item content, it makes more sense here to recode all except sc01_11
mutate(sc01_01_t1_r = sc01_01_t1,
sc01_02_t1_r = sc01_02_t1,
sc01_03_t1_r = sc01_03_t1,
sc01_04_t1_r = sc01_04_t1,
sc01_05_t1_r = sc01_05_t1,
sc01_06_t1_r = sc01_06_t1,
sc01_07_t1_r = sc01_07_t1,
sc01_08_t1_r = sc01_08_t1,
sc01_09_t1_r = sc01_09_t1,
sc01_10_t1_r = sc01_10_t1,
sc01_12_t1_r = sc01_12_t1,
sc01_01_t2_r = sc01_01_t2,
sc01_02_t2_r = sc01_02_t2,
sc01_03_t2_r = sc01_03_t2,
sc01_04_t2_r = sc01_04_t2,
sc01_05_t2_r = sc01_05_t2,
sc01_06_t2_r = sc01_06_t2,
sc01_07_t2_r = sc01_07_t2,
sc01_08_t2_r = sc01_08_t2,
sc01_09_t2_r = sc01_09_t2,
sc01_10_t2_r = sc01_10_t2,
sc01_12_t2_r = sc01_12_t2) %>%
mutate(across(intersect(starts_with("sc01"), ends_with("_r")),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(concept_par1_t1 = rowMeans(across(c(sc01_09_t1_r, sc01_12_t1_r, sc01_03_t1_r, sc01_06_t1_r)), na.rm=T),
concept_par2_t1 = rowMeans(across(c(sc01_08_t1_r, sc01_10_t1_r, sc01_05_t1_r, sc01_11_t1)), na.rm=T),
concept_par3_t1 = rowMeans(across(c(sc01_02_t1_r, sc01_04_t1_r, sc01_01_t1_r, sc01_07_t1_r)), na.rm=T),
concept_par1_t2 = rowMeans(across(c(sc01_09_t2_r, sc01_12_t2_r, sc01_03_t2_r, sc01_06_t2_r)), na.rm=T),
concept_par2_t2 = rowMeans(across(c(sc01_08_t2_r, sc01_10_t2_r, sc01_05_t2_r, sc01_11_t2)), na.rm=T),
concept_par3_t2 = rowMeans(across(c(sc01_02_t2_r, sc01_04_t2_r, sc01_01_t2_r, sc01_07_t2_r)), na.rm=T))
# replace NaN with regular NA
df_sbsa_wide_wb <- df_sbsa_wide_wb %>%
mutate_all(~ifelse(is.nan(.), NA, .))df_sbsa_wide_pers <- df_sbsa %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("bf05", "bf06"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = starts_with(c("bf05", "bf06")))
# colnames(df_sbsa_wide_pers)Check CFA item loadings at T1
cfa_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*bf05_01_t1 + lambda1*bf05_01_t1 + lambda2*bf05_06_t1 + lambda3*bf05_11_t1 + lambda4*bf05_16_t1 + lambda5*bf05_21_t1 + lambda6*bf05_26_t1 + lambda7*bf05_31_t1 + lambda8*bf05_36_t1 + lambda9*bf05_41_t1 + lambda10*bf05_46_t1 + lambda11*bf05_51_t1 + lambda12*bf05_56_t1
# Intercepts
bf05_01_t1 ~ i1*1
bf05_06_t1 ~ 1
bf05_11_t1 ~ 1
bf05_16_t1 ~ 1
bf05_21_t1 ~ 1
bf05_26_t1 ~ 1
bf05_31_t1 ~ 1
bf05_36_t1 ~ 1
bf05_41_t1 ~ 1
bf05_46_t1 ~ 1
bf05_51_t1 ~ 1
bf05_56_t1 ~ 1
# Unique Variances
bf05_01_t1 ~~ bf05_01_t1
bf05_06_t1 ~~ bf05_06_t1
bf05_11_t1 ~~ bf05_11_t1
bf05_16_t1 ~~ bf05_16_t1
bf05_21_t1 ~~ bf05_21_t1
bf05_26_t1 ~~ bf05_26_t1
bf05_31_t1 ~~ bf05_31_t1
bf05_36_t1 ~~ bf05_36_t1
bf05_41_t1 ~~ bf05_41_t1
bf05_46_t1 ~~ bf05_46_t1
bf05_51_t1 ~~ bf05_51_t1
bf05_56_t1 ~~ bf05_56_t1
# Latent Variable Means
extra_curr1 ~ 0*1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
'
fit_cfa_extra_curr <- cfa(cfa_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_extra_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 33 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 2
Model Test User Model:
Test statistic 684.334
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2531.434
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.744
Tucker-Lewis Index (TLI) 0.688
Robust Comparative Fit Index (CFI) 0.744
Robust Tucker-Lewis Index (TLI) 0.688
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12568.224
Loglikelihood unrestricted model (H1) -12226.057
Akaike (AIC) 25208.448
Bayesian (BIC) 25372.492
Sample-size adjusted Bayesian (SABIC) 25258.184
Root Mean Square Error of Approximation:
RMSEA 0.129
90 Percent confidence interval - lower 0.120
90 Percent confidence interval - upper 0.137
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.129
90 Percent confidence interval - lower 0.120
90 Percent confidence interval - upper 0.137
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.075
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
extra_curr1 =~
b05_01_ (lmb1) 0.905 0.045 19.964 0.000
b05_06_ (lmb2) 0.558 0.043 12.961 0.000
b05_11_ (lmb3) -0.288 0.047 -6.054 0.000
b05_16_ (lmb4) -0.681 0.044 -15.352 0.000
b05_21_ (lmb5) 0.745 0.048 15.388 0.000
b05_26_ (lmb6) -0.574 0.051 -11.172 0.000
b05_31_ (lmb7) -0.695 0.040 -17.580 0.000
b05_36_ (lmb8) -0.525 0.044 -12.035 0.000
b05_41_ (lmb9) 0.746 0.044 17.053 0.000
b05_46_ (lm10) 0.855 0.048 17.778 0.000
b05_51_ (lm11) -0.569 0.047 -12.063 0.000
b05_56_ (lm12) 0.621 0.041 15.025 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_01_1 (i1) 2.722 0.048 56.269 0.000
.bf05_06_1 3.058 0.042 72.063 0.000
.bf05_11_1 2.707 0.044 61.144 0.000
.bf05_16_1 3.828 0.044 87.153 0.000
.bf05_21_1 2.733 0.049 56.110 0.000
.bf05_26_1 3.129 0.049 63.404 0.000
.bf05_31_1 4.034 0.041 99.348 0.000
.bf05_36_1 3.161 0.043 74.131 0.000
.bf05_41_1 2.852 0.045 63.934 0.000
.bf05_46_1 2.956 0.050 59.442 0.000
.bf05_51_1 3.178 0.046 69.273 0.000
.bf05_56_1 3.224 0.041 78.112 0.000
extr_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_01_t1 0.828 0.054 15.325 0.000
.bf05_06_t1 0.956 0.054 17.635 0.000
.bf05_11_t1 1.298 0.070 18.537 0.000
.bf05_16_t1 0.895 0.054 16.468 0.000
.bf05_21_t1 1.115 0.066 16.906 0.000
.bf05_26_t1 1.385 0.078 17.831 0.000
.bf05_31_t1 0.678 0.042 15.964 0.000
.bf05_36_t1 1.004 0.056 17.813 0.000
.bf05_41_t1 0.843 0.052 16.106 0.000
.bf05_46_t1 1.009 0.063 16.048 0.000
.bf05_51_t1 1.157 0.065 17.724 0.000
.bf05_56_t1 0.814 0.048 16.935 0.000
extra_curr1 1.000 tidy(fit_cfa_extra_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 extra_curr1 =~ bf05_01_t1 =~ lambda1 0.905 0.0453 0.705
2 extra_curr1 =~ bf05_46_t1 =~ lambda10 0.855 0.0481 0.648
3 extra_curr1 =~ bf05_41_t1 =~ lambda9 0.746 0.0438 0.631
4 extra_curr1 =~ bf05_21_t1 =~ lambda5 0.745 0.0484 0.577
5 extra_curr1 =~ bf05_31_t1 =~ lambda7 -0.695 0.0395 -0.645
6 extra_curr1 =~ bf05_16_t1 =~ lambda4 -0.681 0.0443 -0.584
7 extra_curr1 =~ bf05_56_t1 =~ lambda12 0.621 0.0413 0.567
8 extra_curr1 =~ bf05_26_t1 =~ lambda6 -0.574 0.0514 -0.439
9 extra_curr1 =~ bf05_51_t1 =~ lambda11 -0.569 0.0472 -0.468
10 extra_curr1 =~ bf05_06_t1 =~ lambda2 0.558 0.0431 0.496
11 extra_curr1 =~ bf05_36_t1 =~ lambda8 -0.525 0.0436 -0.464
12 extra_curr1 =~ bf05_11_t1 =~ lambda3 -0.288 0.0475 -0.245Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_31_t1_r = bf05_31_t1,
bf05_16_t1_r = bf05_16_t1,
bf05_26_t1_r = bf05_26_t1,
bf05_51_t1_r = bf05_51_t1,
bf05_36_t1_r = bf05_36_t1,
bf05_11_t1_r = bf05_11_t1,
bf05_31_t2_r = bf05_31_t2,
bf05_16_t2_r = bf05_16_t2,
bf05_26_t2_r = bf05_26_t2,
bf05_51_t2_r = bf05_51_t2,
bf05_36_t2_r = bf05_36_t2,
bf05_11_t2_r = bf05_11_t2) %>%
mutate(across(c(bf05_31_t1_r, bf05_16_t1_r, bf05_26_t1_r, bf05_51_t1_r, bf05_36_t1_r, bf05_11_t1_r,
bf05_31_t2_r, bf05_16_t2_r, bf05_26_t2_r, bf05_51_t2_r, bf05_36_t2_r, bf05_11_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(extra_curr_par1_t1 = rowMeans(across(c(bf05_01_t1, bf05_16_t1_r, bf05_51_t1_r, bf05_11_t1_r)), na.rm=T),
extra_curr_par2_t1 = rowMeans(across(c(bf05_46_t1, bf05_31_t1_r, bf05_26_t1_r, bf05_36_t1_r)), na.rm=T),
extra_curr_par3_t1 = rowMeans(across(c(bf05_41_t1, bf05_21_t1, bf05_56_t1, bf05_06_t1)), na.rm=T),
extra_curr_par1_t2 = rowMeans(across(c(bf05_01_t2, bf05_16_t2_r, bf05_51_t2_r, bf05_11_t2_r)), na.rm=T),
extra_curr_par2_t2 = rowMeans(across(c(bf05_46_t2, bf05_31_t2_r, bf05_26_t2_r, bf05_36_t2_r)), na.rm=T),
extra_curr_par3_t2 = rowMeans(across(c(bf05_41_t2, bf05_21_t2, bf05_56_t2, bf05_06_t2)), na.rm=T))Check CFA item loadings at T1
cfa_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*bf06_01_t1 + lambda1*bf06_01_t1 + lambda2*bf06_06_t1 + lambda3*bf06_11_t1 + lambda4*bf06_16_t1 + lambda5*bf06_21_t1 + lambda6*bf06_26_t1 + lambda7*bf06_31_t1 + lambda8*bf06_36_t1 + lambda9*bf06_41_t1 + lambda10*bf06_46_t1 + lambda11*bf06_51_t1 + lambda12*bf06_56_t1
# Intercepts
bf06_01_t1 ~ i1*1
bf06_06_t1 ~ 1
bf06_11_t1 ~ 1
bf06_16_t1 ~ 1
bf06_21_t1 ~ 1
bf06_26_t1 ~ 1
bf06_31_t1 ~ 1
bf06_36_t1 ~ 1
bf06_41_t1 ~ 1
bf06_46_t1 ~ 1
bf06_51_t1 ~ 1
bf06_56_t1 ~ 1
# Unique Variances
bf06_01_t1 ~~ bf06_01_t1
bf06_06_t1 ~~ bf06_06_t1
bf06_11_t1 ~~ bf06_11_t1
bf06_16_t1 ~~ bf06_16_t1
bf06_21_t1 ~~ bf06_21_t1
bf06_26_t1 ~~ bf06_26_t1
bf06_31_t1 ~~ bf06_31_t1
bf06_36_t1 ~~ bf06_36_t1
bf06_41_t1 ~~ bf06_41_t1
bf06_46_t1 ~~ bf06_46_t1
bf06_51_t1 ~~ bf06_51_t1
bf06_56_t1 ~~ bf06_56_t1
# Latent Variable Means
extra_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
'
fit_cfa_extra_ideal <- cfa(cfa_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_extra_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 18 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 1
Model Test User Model:
Test statistic 352.210
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1114.995
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.716
Tucker-Lewis Index (TLI) 0.653
Robust Comparative Fit Index (CFI) 0.716
Robust Tucker-Lewis Index (TLI) 0.653
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11623.649
Loglikelihood unrestricted model (H1) -11447.544
Akaike (AIC) 23319.298
Bayesian (BIC) 23483.342
Sample-size adjusted Bayesian (SABIC) 23369.034
Root Mean Square Error of Approximation:
RMSEA 0.089
90 Percent confidence interval - lower 0.080
90 Percent confidence interval - upper 0.097
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.948
Robust RMSEA 0.089
90 Percent confidence interval - lower 0.080
90 Percent confidence interval - upper 0.097
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.948
Standardized Root Mean Square Residual:
SRMR 0.063
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
extra_ideal1 =~
b06_01_ (lmb1) 0.357 0.035 10.233 0.000
b06_06_ (lmb2) 0.347 0.041 8.503 0.000
b06_11_ (lmb3) -0.273 0.061 -4.453 0.000
b06_16_ (lmb4) -0.432 0.053 -8.134 0.000
b06_21_ (lmb5) 0.513 0.045 11.284 0.000
b06_26_ (lmb6) -0.464 0.042 -11.163 0.000
b06_31_ (lmb7) -0.500 0.045 -10.998 0.000
b06_36_ (lmb8) -0.482 0.046 -10.548 0.000
b06_41_ (lmb9) 0.378 0.031 12.035 0.000
b06_46_ (lm10) 0.306 0.047 6.526 0.000
b06_51_ (lm11) -0.456 0.045 -10.107 0.000
b06_56_ (lm12) 0.390 0.035 11.269 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_01_1 (i1) 4.249 0.031 138.630 0.000
.bf06_06_1 4.099 0.036 114.344 0.000
.bf06_11_1 2.405 0.052 46.050 0.000
.bf06_16_1 2.848 0.045 63.380 0.000
.bf06_21_1 3.798 0.040 94.342 0.000
.bf06_26_1 1.727 0.037 47.061 0.000
.bf06_31_1 2.136 0.040 53.820 0.000
.bf06_36_1 2.190 0.041 53.964 0.000
.bf06_41_1 4.429 0.028 157.795 0.000
.bf06_46_1 3.760 0.040 94.031 0.000
.bf06_51_1 2.392 0.040 59.998 0.000
.bf06_56_1 4.234 0.031 138.044 0.000
extra_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_01_t1 0.534 0.032 16.891 0.000
.bf06_06_t1 0.785 0.044 17.666 0.000
.bf06_11_t1 1.845 0.100 18.455 0.000
.bf06_16_t1 1.235 0.071 17.413 0.000
.bf06_21_t1 0.878 0.053 16.445 0.000
.bf06_26_t1 0.733 0.045 16.432 0.000
.bf06_31_t1 0.860 0.053 16.348 0.000
.bf06_36_t1 0.927 0.055 16.915 0.000
.bf06_41_t1 0.412 0.026 16.138 0.000
.bf06_46_t1 1.032 0.057 18.028 0.000
.bf06_51_t1 0.911 0.053 17.053 0.000
.bf06_56_t1 0.510 0.031 16.434 0.000
extra_ideal1 1.000 tidy(fit_cfa_extra_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 extra_ideal1 =~ bf06_21_t1 =~ lambda5 0.513 0.0454 0.480
2 extra_ideal1 =~ bf06_31_t1 =~ lambda7 -0.500 0.0454 -0.474
3 extra_ideal1 =~ bf06_36_t1 =~ lambda8 -0.482 0.0457 -0.448
4 extra_ideal1 =~ bf06_26_t1 =~ lambda6 -0.464 0.0416 -0.477
5 extra_ideal1 =~ bf06_51_t1 =~ lambda11 -0.456 0.0451 -0.431
6 extra_ideal1 =~ bf06_16_t1 =~ lambda4 -0.432 0.0531 -0.362
7 extra_ideal1 =~ bf06_56_t1 =~ lambda12 0.390 0.0346 0.480
8 extra_ideal1 =~ bf06_41_t1 =~ lambda9 0.378 0.0314 0.507
9 extra_ideal1 =~ bf06_01_t1 =~ lambda1 0.357 0.0349 0.439
10 extra_ideal1 =~ bf06_06_t1 =~ lambda2 0.347 0.0408 0.365
11 extra_ideal1 =~ bf06_46_t1 =~ lambda10 0.306 0.0469 0.289
12 extra_ideal1 =~ bf06_11_t1 =~ lambda3 -0.273 0.0613 -0.197Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_31_t1_r = bf06_31_t1,
bf06_16_t1_r = bf06_16_t1,
bf06_26_t1_r = bf06_26_t1,
bf06_51_t1_r = bf06_51_t1,
bf06_36_t1_r = bf06_36_t1,
bf06_11_t1_r = bf06_11_t1,
bf06_31_t2_r = bf06_31_t2,
bf06_16_t2_r = bf06_16_t2,
bf06_26_t2_r = bf06_26_t2,
bf06_51_t2_r = bf06_51_t2,
bf06_36_t2_r = bf06_36_t2,
bf06_11_t2_r = bf06_11_t2) %>%
mutate(across(c(bf06_31_t1_r, bf06_16_t1_r, bf06_26_t1_r, bf06_51_t1_r, bf06_36_t1_r, bf06_11_t1_r,
bf06_31_t2_r, bf06_16_t2_r, bf06_26_t2_r, bf06_51_t2_r, bf06_36_t2_r, bf06_11_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(extra_ideal_par1_t1 = rowMeans(across(c(bf06_21_t1, bf06_16_t1_r, bf06_01_t1, bf06_11_t1_r)), na.rm=T),
extra_ideal_par2_t1 = rowMeans(across(c(bf06_31_t1_r, bf06_51_t1_r, bf06_41_t1, bf06_46_t1)), na.rm=T),
extra_ideal_par3_t1 = rowMeans(across(c(bf06_36_t1_r, bf06_26_t1_r, bf06_56_t1, bf06_06_t1)), na.rm=T),
extra_ideal_par1_t2 = rowMeans(across(c(bf06_21_t2, bf06_16_t2_r, bf06_01_t2, bf06_11_t2_r)), na.rm=T),
extra_ideal_par2_t2 = rowMeans(across(c(bf06_31_t2_r, bf06_51_t2_r, bf06_41_t2, bf06_46_t2)), na.rm=T),
extra_ideal_par3_t2 = rowMeans(across(c(bf06_36_t2_r, bf06_26_t2_r, bf06_56_t2, bf06_06_t2)), na.rm=T))Check CFA item loadings at T1
cfa_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*bf05_02_t1 + lambda1*bf05_02_t1 + lambda2*bf05_07_t1 + lambda3*bf05_12_t1 + lambda4*bf05_17_t1 + lambda5*bf05_22_t1 + lambda6*bf05_27_t1 + lambda7*bf05_32_t1 + lambda8*bf05_37_t1 + lambda9*bf05_42_t1 + lambda10*bf05_47_t1 + lambda11*bf05_52_t1 + lambda12*bf05_57_t1
# Intercepts
bf05_02_t1 ~ i1*1
bf05_07_t1 ~ 1
bf05_12_t1 ~ 1
bf05_17_t1 ~ 1
bf05_22_t1 ~ 1
bf05_27_t1 ~ 1
bf05_32_t1 ~ 1
bf05_37_t1 ~ 1
bf05_42_t1 ~ 1
bf05_47_t1 ~ 1
bf05_52_t1 ~ 1
bf05_57_t1 ~ 1
# Unique Variances
bf05_02_t1 ~~ bf05_02_t1
bf05_07_t1 ~~ bf05_07_t1
bf05_12_t1 ~~ bf05_12_t1
bf05_17_t1 ~~ bf05_17_t1
bf05_22_t1 ~~ bf05_22_t1
bf05_27_t1 ~~ bf05_27_t1
bf05_32_t1 ~~ bf05_32_t1
bf05_37_t1 ~~ bf05_37_t1
bf05_42_t1 ~~ bf05_42_t1
bf05_47_t1 ~~ bf05_47_t1
bf05_52_t1 ~~ bf05_52_t1
bf05_57_t1 ~~ bf05_57_t1
# Latent Variable Means
agree_curr1 ~ 0*1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
'
fit_cfa_agree_curr <- cfa(cfa_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_agree_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 42 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 5
Model Test User Model:
Test statistic 397.070
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1779.776
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.800
Tucker-Lewis Index (TLI) 0.755
Robust Comparative Fit Index (CFI) 0.800
Robust Tucker-Lewis Index (TLI) 0.755
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12059.241
Loglikelihood unrestricted model (H1) -11860.706
Akaike (AIC) 24190.482
Bayesian (BIC) 24354.526
Sample-size adjusted Bayesian (SABIC) 24240.218
Root Mean Square Error of Approximation:
RMSEA 0.095
90 Percent confidence interval - lower 0.086
90 Percent confidence interval - upper 0.104
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.998
Robust RMSEA 0.095
90 Percent confidence interval - lower 0.086
90 Percent confidence interval - upper 0.104
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.998
Standardized Root Mean Square Residual:
SRMR 0.062
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
agree_curr1 =~
b05_02_ (lmb1) 0.556 0.036 15.654 0.000
b05_07_ (lmb2) 0.462 0.029 15.657 0.000
b05_12_ (lmb3) -0.575 0.049 -11.847 0.000
b05_17_ (lmb4) -0.253 0.059 -4.265 0.000
b05_22_ (lmb5) -0.635 0.049 -12.979 0.000
b05_27_ (lmb6) 0.593 0.046 12.857 0.000
b05_32_ (lmb7) 0.467 0.035 13.511 0.000
b05_37_ (lmb8) -0.733 0.048 -15.243 0.000
b05_42_ (lmb9) -0.312 0.048 -6.548 0.000
b05_47_ (lm10) -0.736 0.049 -14.932 0.000
b05_52_ (lm11) 0.478 0.031 15.343 0.000
b05_57_ (lm12) 0.580 0.048 12.197 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_02_1 (i1) 4.132 0.035 117.713 0.000
.bf05_07_1 4.412 0.029 150.971 0.000
.bf05_12_1 2.935 0.046 63.427 0.000
.bf05_17_1 2.845 0.054 52.892 0.000
.bf05_22_1 2.302 0.047 49.028 0.000
.bf05_27_1 3.647 0.044 82.286 0.000
.bf05_32_1 3.951 0.034 117.879 0.000
.bf05_37_1 2.592 0.047 54.777 0.000
.bf05_42_1 3.604 0.043 83.442 0.000
.bf05_47_1 2.500 0.049 51.498 0.000
.bf05_52_1 4.222 0.031 137.343 0.000
.bf05_57_1 3.112 0.045 68.733 0.000
agre_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_02_t1 0.559 0.035 16.173 0.000
.bf05_07_t1 0.387 0.024 16.224 0.000
.bf05_12_t1 1.176 0.067 17.474 0.000
.bf05_17_t1 1.973 0.106 18.620 0.000
.bf05_22_t1 1.147 0.067 17.071 0.000
.bf05_27_t1 1.030 0.060 17.182 0.000
.bf05_32_t1 0.572 0.034 17.041 0.000
.bf05_37_t1 1.039 0.064 16.324 0.000
.bf05_42_t1 1.216 0.066 18.366 0.000
.bf05_47_t1 1.118 0.067 16.592 0.000
.bf05_52_t1 0.436 0.027 16.320 0.000
.bf05_57_t1 1.107 0.064 17.324 0.000
agree_curr1 1.000 tidy(fit_cfa_agree_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 agree_curr1 =~ bf05_47_t1 =~ lambda10 -0.736 0.0493 -0.571
2 agree_curr1 =~ bf05_37_t1 =~ lambda8 -0.733 0.0481 -0.584
3 agree_curr1 =~ bf05_22_t1 =~ lambda5 -0.635 0.0489 -0.510
4 agree_curr1 =~ bf05_27_t1 =~ lambda6 0.593 0.0461 0.504
5 agree_curr1 =~ bf05_57_t1 =~ lambda12 0.580 0.0475 0.483
6 agree_curr1 =~ bf05_12_t1 =~ lambda3 -0.575 0.0486 -0.469
7 agree_curr1 =~ bf05_02_t1 =~ lambda1 0.556 0.0355 0.597
8 agree_curr1 =~ bf05_52_t1 =~ lambda11 0.478 0.0312 0.586
9 agree_curr1 =~ bf05_32_t1 =~ lambda7 0.467 0.0346 0.525
10 agree_curr1 =~ bf05_07_t1 =~ lambda2 0.462 0.0295 0.596
11 agree_curr1 =~ bf05_42_t1 =~ lambda9 -0.312 0.0476 -0.272
12 agree_curr1 =~ bf05_17_t1 =~ lambda4 -0.253 0.0592 -0.177Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_47_t1_r = bf05_47_t1,
bf05_37_t1_r = bf05_37_t1,
bf05_22_t1_r = bf05_22_t1,
bf05_12_t1_r = bf05_12_t1,
bf05_42_t1_r = bf05_42_t1,
bf05_17_t1_r = bf05_17_t1,
bf05_47_t2_r = bf05_47_t2,
bf05_37_t2_r = bf05_37_t2,
bf05_22_t2_r = bf05_22_t2,
bf05_12_t2_r = bf05_12_t2,
bf05_42_t2_r = bf05_42_t2,
bf05_17_t2_r = bf05_17_t2) %>%
mutate(across(c(bf05_47_t1_r, bf05_37_t1_r, bf05_22_t1_r, bf05_12_t1_r, bf05_42_t1_r, bf05_17_t1_r,
bf05_47_t2_r, bf05_37_t2_r, bf05_22_t2_r, bf05_12_t2_r, bf05_42_t2_r, bf05_17_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(agree_curr_par1_t1 = rowMeans(across(c(bf05_47_t1_r, bf05_12_t1_r, bf05_32_t1, bf05_17_t1)), na.rm=T),
agree_curr_par2_t1 = rowMeans(across(c(bf05_37_t1_r, bf05_57_t1, bf05_52_t1, bf05_42_t1)), na.rm=T),
agree_curr_par3_t1 = rowMeans(across(c(bf05_22_t1_r, bf05_27_t1, bf05_02_t1, bf05_07_t1)), na.rm=T),
agree_curr_par1_t2 = rowMeans(across(c(bf05_47_t2_r, bf05_12_t2_r, bf05_32_t2, bf05_17_t2)), na.rm=T),
agree_curr_par2_t2 = rowMeans(across(c(bf05_37_t2_r, bf05_57_t2, bf05_52_t2, bf05_42_t2)), na.rm=T),
agree_curr_par3_t2 = rowMeans(across(c(bf05_22_t2_r, bf05_27_t2, bf05_02_t2, bf05_07_t2)), na.rm=T))Check CFA item loadings at T1
cfa_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*bf06_02_t1 + lambda1*bf06_02_t1 + lambda2*bf06_07_t1 + lambda3*bf06_12_t1 + lambda4*bf06_17_t1 + lambda5*bf06_22_t1 + lambda6*bf06_27_t1 + lambda7*bf06_32_t1 + lambda8*bf06_37_t1 + lambda9*bf06_42_t1 + lambda10*bf06_47_t1 + lambda11*bf06_52_t1 + lambda12*bf06_57_t1
# Intercepts
bf06_02_t1 ~ i1*1
bf06_07_t1 ~ 1
bf06_12_t1 ~ 1
bf06_17_t1 ~ 1
bf06_22_t1 ~ 1
bf06_27_t1 ~ 1
bf06_32_t1 ~ 1
bf06_37_t1 ~ 1
bf06_42_t1 ~ 1
bf06_47_t1 ~ 1
bf06_52_t1 ~ 1
bf06_57_t1 ~ 1
# Unique Variances
bf06_02_t1 ~~ bf06_02_t1
bf06_07_t1 ~~ bf06_07_t1
bf06_12_t1 ~~ bf06_12_t1
bf06_17_t1 ~~ bf06_17_t1
bf06_22_t1 ~~ bf06_22_t1
bf06_27_t1 ~~ bf06_27_t1
bf06_32_t1 ~~ bf06_32_t1
bf06_37_t1 ~~ bf06_37_t1
bf06_42_t1 ~~ bf06_42_t1
bf06_47_t1 ~~ bf06_47_t1
bf06_52_t1 ~~ bf06_52_t1
bf06_57_t1 ~~ bf06_57_t1
# Latent Variable Means
agree_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
'
fit_cfa_agree_ideal <- cfa(cfa_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_agree_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 38 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 3
Model Test User Model:
Test statistic 207.020
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1676.172
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.905
Tucker-Lewis Index (TLI) 0.884
Robust Comparative Fit Index (CFI) 0.905
Robust Tucker-Lewis Index (TLI) 0.884
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11321.510
Loglikelihood unrestricted model (H1) -11218.000
Akaike (AIC) 22715.020
Bayesian (BIC) 22879.064
Sample-size adjusted Bayesian (SABIC) 22764.757
Root Mean Square Error of Approximation:
RMSEA 0.063
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.073
P-value H_0: RMSEA <= 0.050 0.008
P-value H_0: RMSEA >= 0.080 0.001
Robust RMSEA 0.063
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.073
P-value H_0: Robust RMSEA <= 0.050 0.008
P-value H_0: Robust RMSEA >= 0.080 0.001
Standardized Root Mean Square Residual:
SRMR 0.043
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
agree_ideal1 =~
b06_02_ (lmb1) 0.547 0.036 15.247 0.000
b06_07_ (lmb2) 0.376 0.027 13.838 0.000
b06_12_ (lmb3) -0.418 0.039 -10.640 0.000
b06_17_ (lmb4) -0.155 0.064 -2.426 0.015
b06_22_ (lmb5) -0.404 0.044 -9.184 0.000
b06_27_ (lmb6) 0.637 0.039 16.366 0.000
b06_32_ (lmb7) 0.516 0.037 13.786 0.000
b06_37_ (lmb8) -0.532 0.035 -15.064 0.000
b06_42_ (lmb9) -0.417 0.047 -8.803 0.000
b06_47_ (lm10) -0.710 0.042 -17.026 0.000
b06_52_ (lm11) 0.477 0.029 16.461 0.000
b06_57_ (lm12) 0.601 0.039 15.498 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_02_1 (i1) 4.222 0.036 118.086 0.000
.bf06_07_1 4.616 0.027 173.609 0.000
.bf06_12_1 1.952 0.037 52.218 0.000
.bf06_17_1 2.818 0.058 48.481 0.000
.bf06_22_1 1.906 0.041 46.199 0.000
.bf06_27_1 3.966 0.039 101.100 0.000
.bf06_32_1 4.256 0.037 115.812 0.000
.bf06_37_1 1.703 0.035 48.588 0.000
.bf06_42_1 2.766 0.044 62.568 0.000
.bf06_47_1 1.891 0.042 44.701 0.000
.bf06_52_1 4.472 0.029 152.983 0.000
.bf06_57_1 3.709 0.039 95.898 0.000
agree_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_02_t1 0.601 0.036 16.725 0.000
.bf06_07_t1 0.356 0.021 17.075 0.000
.bf06_12_t1 0.809 0.045 17.873 0.000
.bf06_17_t1 2.355 0.126 18.720 0.000
.bf06_22_t1 1.036 0.057 18.105 0.000
.bf06_27_t1 0.677 0.042 16.251 0.000
.bf06_32_t1 0.683 0.040 17.155 0.000
.bf06_37_t1 0.582 0.035 16.735 0.000
.bf06_42_t1 1.202 0.066 18.140 0.000
.bf06_47_t1 0.756 0.047 15.972 0.000
.bf06_52_t1 0.374 0.023 16.214 0.000
.bf06_57_t1 0.692 0.042 16.577 0.000
agree_ideal1 1.000 tidy(fit_cfa_agree_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 agree_ideal1 =~ bf06_47_t1 =~ lambda10 -0.710 0.0417 -0.632
2 agree_ideal1 =~ bf06_27_t1 =~ lambda6 0.637 0.0389 0.612
3 agree_ideal1 =~ bf06_57_t1 =~ lambda12 0.601 0.0388 0.586
4 agree_ideal1 =~ bf06_02_t1 =~ lambda1 0.547 0.0359 0.577
5 agree_ideal1 =~ bf06_37_t1 =~ lambda8 -0.532 0.0353 -0.572
6 agree_ideal1 =~ bf06_32_t1 =~ lambda7 0.516 0.0374 0.530
7 agree_ideal1 =~ bf06_52_t1 =~ lambda11 0.477 0.0290 0.615
8 agree_ideal1 =~ bf06_12_t1 =~ lambda3 -0.418 0.0393 -0.422
9 agree_ideal1 =~ bf06_42_t1 =~ lambda9 -0.417 0.0474 -0.355
10 agree_ideal1 =~ bf06_22_t1 =~ lambda5 -0.404 0.0440 -0.369
11 agree_ideal1 =~ bf06_07_t1 =~ lambda2 0.376 0.0272 0.533
12 agree_ideal1 =~ bf06_17_t1 =~ lambda4 -0.155 0.0639 -0.101Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_47_t1_r = bf06_47_t1,
bf06_37_t1_r = bf06_37_t1,
bf06_22_t1_r = bf06_22_t1,
bf06_12_t1_r = bf06_12_t1,
bf06_42_t1_r = bf06_42_t1,
bf06_17_t1_r = bf06_17_t1,
bf06_47_t2_r = bf06_47_t2,
bf06_37_t2_r = bf06_37_t2,
bf06_22_t2_r = bf06_22_t2,
bf06_12_t2_r = bf06_12_t2,
bf06_42_t2_r = bf06_42_t2,
bf06_17_t2_r = bf06_17_t2) %>%
mutate(across(c(bf06_47_t1_r, bf06_37_t1_r, bf06_22_t1_r, bf06_12_t1_r, bf06_42_t1_r, bf06_17_t1_r,
bf06_47_t2_r, bf06_37_t2_r, bf06_22_t2_r, bf06_12_t2_r, bf06_42_t2_r, bf06_17_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(agree_ideal_par1_t1 = rowMeans(across(c(bf06_47_t1_r, bf06_32_t1, bf06_42_t1_r, bf06_17_t1_r)), na.rm=T),
agree_ideal_par2_t1 = rowMeans(across(c(bf06_27_t1, bf06_37_t1_r, bf06_12_t1_r, bf06_07_t1)), na.rm=T),
agree_ideal_par3_t1 = rowMeans(across(c(bf06_57_t1, bf06_02_t1, bf06_52_t1, bf06_22_t1_r)), na.rm=T),
agree_ideal_par1_t2 = rowMeans(across(c(bf06_47_t2_r, bf06_32_t2, bf06_42_t2_r, bf06_17_t2_r)), na.rm=T),
agree_ideal_par2_t2 = rowMeans(across(c(bf06_27_t2, bf06_37_t2_r, bf06_12_t2_r, bf06_07_t2)), na.rm=T),
agree_ideal_par3_t2 = rowMeans(across(c(bf06_57_t2, bf06_02_t2, bf06_52_t2, bf06_22_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*bf05_03_t1 + lambda1*bf05_03_t1 + lambda2*bf05_08_t1 + lambda3*bf05_13_t1 + lambda4*bf05_18_t1 + lambda5*bf05_23_t1 + lambda6*bf05_28_t1 + lambda7*bf05_33_t1 + lambda8*bf05_38_t1 + lambda9*bf05_43_t1 + lambda10*bf05_48_t1 + lambda11*bf05_53_t1 + lambda12*bf05_58_t1
# Intercepts
bf05_03_t1 ~ i1*1
bf05_08_t1 ~ 1
bf05_13_t1 ~ 1
bf05_18_t1 ~ 1
bf05_23_t1 ~ 1
bf05_28_t1 ~ 1
bf05_33_t1 ~ 1
bf05_38_t1 ~ 1
bf05_43_t1 ~ 1
bf05_48_t1 ~ 1
bf05_53_t1 ~ 1
bf05_58_t1 ~ 1
# Unique Variances
bf05_03_t1 ~~ bf05_03_t1
bf05_08_t1 ~~ bf05_08_t1
bf05_13_t1 ~~ bf05_13_t1
bf05_18_t1 ~~ bf05_18_t1
bf05_23_t1 ~~ bf05_23_t1
bf05_28_t1 ~~ bf05_28_t1
bf05_33_t1 ~~ bf05_33_t1
bf05_38_t1 ~~ bf05_38_t1
bf05_43_t1 ~~ bf05_43_t1
bf05_48_t1 ~~ bf05_48_t1
bf05_53_t1 ~~ bf05_53_t1
bf05_58_t1 ~~ bf05_58_t1
# Latent Variable Means
consc_curr1 ~ 0*1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
'
fit_cfa_consc_curr <- cfa(cfa_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_consc_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 39 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 2
Model Test User Model:
Test statistic 514.063
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3020.731
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.844
Tucker-Lewis Index (TLI) 0.810
Robust Comparative Fit Index (CFI) 0.844
Robust Tucker-Lewis Index (TLI) 0.810
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12199.493
Loglikelihood unrestricted model (H1) -11942.461
Akaike (AIC) 24470.986
Bayesian (BIC) 24635.030
Sample-size adjusted Bayesian (SABIC) 24520.722
Root Mean Square Error of Approximation:
RMSEA 0.110
90 Percent confidence interval - lower 0.101
90 Percent confidence interval - upper 0.119
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.110
90 Percent confidence interval - lower 0.101
90 Percent confidence interval - upper 0.119
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.061
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
consc_curr1 =~
b05_03_ (lmb1) 1.070 0.049 22.011 0.000
b05_08_ (lmb2) 0.781 0.046 17.094 0.000
b05_13_ (lmb3) -0.443 0.041 -10.767 0.000
b05_18_ (lmb4) -0.855 0.042 -20.576 0.000
b05_23_ (lmb5) 0.789 0.048 16.467 0.000
b05_28_ (lmb6) 0.704 0.046 15.168 0.000
b05_33_ (lmb7) -0.872 0.042 -20.697 0.000
b05_38_ (lmb8) -0.654 0.036 -17.983 0.000
b05_43_ (lmb9) -0.463 0.035 -13.219 0.000
b05_48_ (lm10) 0.766 0.044 17.481 0.000
b05_53_ (lm11) -0.672 0.041 -16.473 0.000
b05_58_ (lm12) 0.622 0.048 12.969 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_03_1 (i1) 2.901 0.054 53.495 0.000
.bf05_08_1 3.455 0.048 72.076 0.000
.bf05_13_1 3.597 0.040 88.997 0.000
.bf05_18_1 3.548 0.045 78.125 0.000
.bf05_23_1 3.310 0.050 66.531 0.000
.bf05_28_1 2.847 0.048 59.789 0.000
.bf05_33_1 3.354 0.046 72.830 0.000
.bf05_38_1 3.594 0.038 93.523 0.000
.bf05_43_1 3.956 0.035 112.611 0.000
.bf05_48_1 2.158 0.046 46.799 0.000
.bf05_53_1 3.673 0.042 86.641 0.000
.bf05_58_1 2.794 0.048 58.076 0.000
cnsc_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_03_t1 0.923 0.059 15.538 0.000
.bf05_08_t1 1.007 0.059 17.200 0.000
.bf05_13_t1 0.954 0.052 18.226 0.000
.bf05_18_t1 0.721 0.045 16.063 0.000
.bf05_23_t1 1.120 0.065 17.261 0.000
.bf05_28_t1 1.100 0.063 17.582 0.000
.bf05_33_t1 0.732 0.046 15.905 0.000
.bf05_38_t1 0.612 0.036 16.837 0.000
.bf05_43_t1 0.655 0.037 17.876 0.000
.bf05_48_t1 0.909 0.053 17.033 0.000
.bf05_53_t1 0.813 0.047 17.252 0.000
.bf05_58_t1 1.243 0.069 17.965 0.000
consc_curr1 1.000 tidy(fit_cfa_consc_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 consc_curr1 =~ bf05_03_t1 =~ lambda1 1.07 0.0486 0.744
2 consc_curr1 =~ bf05_33_t1 =~ lambda7 -0.872 0.0421 -0.714
3 consc_curr1 =~ bf05_18_t1 =~ lambda4 -0.855 0.0416 -0.710
4 consc_curr1 =~ bf05_23_t1 =~ lambda5 0.789 0.0479 0.597
5 consc_curr1 =~ bf05_08_t1 =~ lambda2 0.781 0.0457 0.614
6 consc_curr1 =~ bf05_48_t1 =~ lambda10 0.766 0.0438 0.626
7 consc_curr1 =~ bf05_28_t1 =~ lambda6 0.704 0.0464 0.557
8 consc_curr1 =~ bf05_53_t1 =~ lambda11 -0.672 0.0408 -0.598
9 consc_curr1 =~ bf05_38_t1 =~ lambda8 -0.654 0.0364 -0.642
10 consc_curr1 =~ bf05_58_t1 =~ lambda12 0.622 0.0479 0.487
11 consc_curr1 =~ bf05_43_t1 =~ lambda9 -0.463 0.0350 -0.497
12 consc_curr1 =~ bf05_13_t1 =~ lambda3 -0.443 0.0411 -0.413Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_03_t1_r = bf05_03_t1,
bf05_23_t1_r = bf05_23_t1,
bf05_48_t1_r = bf05_48_t1,
bf05_08_t1_r = bf05_08_t1,
bf05_28_t1_r = bf05_28_t1,
bf05_58_t1_r = bf05_58_t1,
bf05_03_t2_r = bf05_03_t2,
bf05_23_t2_r = bf05_23_t2,
bf05_48_t2_r = bf05_48_t2,
bf05_08_t2_r = bf05_08_t2,
bf05_28_t2_r = bf05_28_t2,
bf05_58_t2_r = bf05_58_t2) %>%
mutate(across(c(bf05_03_t1_r, bf05_23_t1_r, bf05_48_t1_r, bf05_08_t1_r, bf05_28_t1_r, bf05_58_t1_r,
bf05_03_t2_r, bf05_23_t2_r, bf05_48_t2_r, bf05_08_t2_r, bf05_28_t2_r, bf05_58_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(consc_curr_par1_t1 = rowMeans(across(c(bf05_03_t1_r, bf05_48_t1_r, bf05_38_t1, bf05_13_t1)), na.rm=T),
consc_curr_par2_t1 = rowMeans(across(c(bf05_33_t1, bf05_08_t1_r, bf05_53_t1, bf05_43_t1)), na.rm=T),
consc_curr_par3_t1 = rowMeans(across(c(bf05_18_t1, bf05_23_t1_r, bf05_28_t1_r, bf05_58_t1_r)), na.rm=T),
consc_curr_par1_t2 = rowMeans(across(c(bf05_03_t2_r, bf05_48_t2_r, bf05_38_t2, bf05_13_t2)), na.rm=T),
consc_curr_par2_t2 = rowMeans(across(c(bf05_33_t2, bf05_08_t2_r, bf05_53_t2, bf05_43_t2)), na.rm=T),
consc_curr_par3_t2 = rowMeans(across(c(bf05_18_t2, bf05_23_t2_r, bf05_28_t2_r, bf05_58_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*bf06_03_t1 + lambda1*bf06_03_t1 + lambda2*bf06_08_t1 + lambda3*bf06_13_t1 + lambda4*bf06_18_t1 + lambda5*bf06_23_t1 + lambda6*bf06_28_t1 + lambda7*bf06_33_t1 + lambda8*bf06_38_t1 + lambda9*bf06_43_t1 + lambda10*bf06_48_t1 + lambda11*bf06_53_t1 + lambda12*bf06_58_t1
# Intercepts
bf06_03_t1 ~ i1*1
bf06_08_t1 ~ 1
bf06_13_t1 ~ 1
bf06_18_t1 ~ 1
bf06_23_t1 ~ 1
bf06_28_t1 ~ 1
bf06_33_t1 ~ 1
bf06_38_t1 ~ 1
bf06_43_t1 ~ 1
bf06_48_t1 ~ 1
bf06_53_t1 ~ 1
bf06_58_t1 ~ 1
# Unique Variances
bf06_03_t1 ~~ bf06_03_t1
bf06_08_t1 ~~ bf06_08_t1
bf06_13_t1 ~~ bf06_13_t1
bf06_18_t1 ~~ bf06_18_t1
bf06_23_t1 ~~ bf06_23_t1
bf06_28_t1 ~~ bf06_28_t1
bf06_33_t1 ~~ bf06_33_t1
bf06_38_t1 ~~ bf06_38_t1
bf06_43_t1 ~~ bf06_43_t1
bf06_48_t1 ~~ bf06_48_t1
bf06_53_t1 ~~ bf06_53_t1
bf06_58_t1 ~~ bf06_58_t1
# Latent Variable Means
consc_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
'
fit_cfa_consc_ideal <- cfa(cfa_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_consc_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 21 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 1
Model Test User Model:
Test statistic 228.997
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1639.961
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.889
Tucker-Lewis Index (TLI) 0.864
Robust Comparative Fit Index (CFI) 0.889
Robust Tucker-Lewis Index (TLI) 0.864
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10137.985
Loglikelihood unrestricted model (H1) -10023.487
Akaike (AIC) 20347.971
Bayesian (BIC) 20512.015
Sample-size adjusted Bayesian (SABIC) 20397.707
Root Mean Square Error of Approximation:
RMSEA 0.068
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.077
P-value H_0: RMSEA <= 0.050 0.001
P-value H_0: RMSEA >= 0.080 0.014
Robust RMSEA 0.068
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.077
P-value H_0: Robust RMSEA <= 0.050 0.001
P-value H_0: Robust RMSEA >= 0.080 0.014
Standardized Root Mean Square Residual:
SRMR 0.046
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
consc_ideal1 =~
b06_03_ (lmb1) 0.394 0.048 8.114 0.000
b06_08_ (lmb2) 0.416 0.038 10.995 0.000
b06_13_ (lmb3) -0.385 0.042 -9.141 0.000
b06_18_ (lmb4) -0.383 0.032 -12.025 0.000
b06_23_ (lmb5) 0.457 0.036 12.823 0.000
b06_28_ (lmb6) 0.548 0.043 12.729 0.000
b06_33_ (lmb7) -0.470 0.030 -15.706 0.000
b06_38_ (lmb8) -0.415 0.028 -14.980 0.000
b06_43_ (lmb9) -0.397 0.026 -15.113 0.000
b06_48_ (lm10) 0.438 0.029 14.955 0.000
b06_53_ (lm11) -0.452 0.027 -16.639 0.000
b06_58_ (lm12) 0.456 0.037 12.430 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_03_1 (i1) 1.737 0.045 38.706 0.000
.bf06_08_1 1.629 0.036 45.545 0.000
.bf06_13_1 4.246 0.039 107.963 0.000
.bf06_18_1 4.337 0.030 142.469 0.000
.bf06_23_1 1.526 0.034 44.384 0.000
.bf06_28_1 1.996 0.042 48.050 0.000
.bf06_33_1 4.411 0.030 148.301 0.000
.bf06_38_1 4.622 0.027 168.827 0.000
.bf06_43_1 4.555 0.026 175.521 0.000
.bf06_48_1 1.405 0.029 48.512 0.000
.bf06_53_1 4.564 0.027 167.234 0.000
.bf06_58_1 1.744 0.035 49.398 0.000
consc_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_03_t1 1.263 0.069 18.213 0.000
.bf06_08_t1 0.728 0.041 17.695 0.000
.bf06_13_t1 0.941 0.052 18.074 0.000
.bf06_18_t1 0.506 0.029 17.472 0.000
.bf06_23_t1 0.623 0.036 17.289 0.000
.bf06_28_t1 0.914 0.053 17.325 0.000
.bf06_33_t1 0.402 0.025 16.327 0.000
.bf06_38_t1 0.355 0.021 16.619 0.000
.bf06_43_t1 0.317 0.019 16.556 0.000
.bf06_48_t1 0.398 0.024 16.666 0.000
.bf06_53_t1 0.320 0.020 15.912 0.000
.bf06_58_t1 0.670 0.038 17.419 0.000
consc_ideal1 1.000 tidy(fit_cfa_consc_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 consc_ideal1 =~ bf06_28_t1 =~ lambda6 0.548 0.0431 0.497
2 consc_ideal1 =~ bf06_33_t1 =~ lambda7 -0.470 0.0299 -0.595
3 consc_ideal1 =~ bf06_23_t1 =~ lambda5 0.457 0.0356 0.501
4 consc_ideal1 =~ bf06_58_t1 =~ lambda12 0.456 0.0367 0.487
5 consc_ideal1 =~ bf06_53_t1 =~ lambda11 -0.452 0.0272 -0.624
6 consc_ideal1 =~ bf06_48_t1 =~ lambda10 0.438 0.0293 0.570
7 consc_ideal1 =~ bf06_08_t1 =~ lambda2 0.416 0.0378 0.438
8 consc_ideal1 =~ bf06_38_t1 =~ lambda8 -0.415 0.0277 -0.572
9 consc_ideal1 =~ bf06_43_t1 =~ lambda9 -0.397 0.0263 -0.576
10 consc_ideal1 =~ bf06_03_t1 =~ lambda1 0.394 0.0485 0.330
11 consc_ideal1 =~ bf06_13_t1 =~ lambda3 -0.385 0.0421 -0.369
12 consc_ideal1 =~ bf06_18_t1 =~ lambda4 -0.383 0.0318 -0.474Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_03_t1_r = bf06_03_t1,
bf06_23_t1_r = bf06_23_t1,
bf06_48_t1_r = bf06_48_t1,
bf06_08_t1_r = bf06_08_t1,
bf06_28_t1_r = bf06_28_t1,
bf06_58_t1_r = bf06_58_t1,
bf06_03_t2_r = bf06_03_t2,
bf06_23_t2_r = bf06_23_t2,
bf06_48_t2_r = bf06_48_t2,
bf06_08_t2_r = bf06_08_t2,
bf06_28_t2_r = bf06_28_t2,
bf06_58_t2_r = bf06_58_t2) %>%
mutate(across(c(bf06_03_t1_r, bf06_23_t1_r, bf06_48_t1_r, bf06_08_t1_r, bf06_28_t1_r, bf06_58_t1_r,
bf06_03_t2_r, bf06_23_t2_r, bf06_48_t2_r, bf06_08_t2_r, bf06_28_t2_r, bf06_58_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(consc_ideal_par1_t1 = rowMeans(across(c(bf06_28_t1_r, bf06_48_t1_r, bf06_43_t1, bf06_18_t1)), na.rm=T),
consc_ideal_par2_t1 = rowMeans(across(c(bf06_33_t1, bf06_53_t1, bf06_38_t1, bf06_13_t1)), na.rm=T),
consc_ideal_par3_t1 = rowMeans(across(c(bf06_23_t1_r, bf06_58_t1_r, bf06_08_t1_r, bf06_03_t1_r)), na.rm=T),
consc_ideal_par1_t2 = rowMeans(across(c(bf06_28_t2_r, bf06_48_t2_r, bf06_43_t2, bf06_18_t2)), na.rm=T),
consc_ideal_par2_t2 = rowMeans(across(c(bf06_33_t2, bf06_53_t2, bf06_38_t2, bf06_13_t2)), na.rm=T),
consc_ideal_par3_t2 = rowMeans(across(c(bf06_23_t2_r, bf06_58_t2_r, bf06_08_t2_r, bf06_03_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_neuro_curr <- '
#| warning: false
# Define the latent factors
neuro_curr1 =~ NA*bf05_04_t1 + lambda1*bf05_04_t1 + lambda2*bf05_09_t1 + lambda3*bf05_14_t1 + lambda4*bf05_19_t1 + lambda5*bf05_24_t1 + lambda6*bf05_29_t1 + lambda7*bf05_34_t1 + lambda8*bf05_39_t1 + lambda9*bf05_44_t1 + lambda10*bf05_49_t1 + lambda11*bf05_54_t1 + lambda12*bf05_59_t1
# Intercepts
bf05_04_t1 ~ i1*1
bf05_09_t1 ~ 1
bf05_14_t1 ~ 1
bf05_19_t1 ~ 1
bf05_24_t1 ~ 1
bf05_29_t1 ~ 1
bf05_34_t1 ~ 1
bf05_39_t1 ~ 1
bf05_44_t1 ~ 1
bf05_49_t1 ~ 1
bf05_54_t1 ~ 1
bf05_59_t1 ~ 1
# Unique Variances
bf05_04_t1 ~~ bf05_04_t1
bf05_09_t1 ~~ bf05_09_t1
bf05_14_t1 ~~ bf05_14_t1
bf05_19_t1 ~~ bf05_19_t1
bf05_24_t1 ~~ bf05_24_t1
bf05_29_t1 ~~ bf05_29_t1
bf05_34_t1 ~~ bf05_34_t1
bf05_39_t1 ~~ bf05_39_t1
bf05_44_t1 ~~ bf05_44_t1
bf05_49_t1 ~~ bf05_49_t1
bf05_54_t1 ~~ bf05_54_t1
bf05_59_t1 ~~ bf05_59_t1
# Latent Variable Means
neuro_curr1 ~ 0*1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
'
fit_cfa_neuro_curr <- cfa(cfa_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
16 41 91 317 381 382 414 444 505 606 705summary(fit_cfa_neuro_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 4
Model Test User Model:
Test statistic 654.794
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3599.996
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.830
Tucker-Lewis Index (TLI) 0.792
Robust Comparative Fit Index (CFI) 0.830
Robust Tucker-Lewis Index (TLI) 0.792
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12392.351
Loglikelihood unrestricted model (H1) -12064.954
Akaike (AIC) 24856.701
Bayesian (BIC) 25020.745
Sample-size adjusted Bayesian (SABIC) 24906.437
Root Mean Square Error of Approximation:
RMSEA 0.126
90 Percent confidence interval - lower 0.117
90 Percent confidence interval - upper 0.134
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.126
90 Percent confidence interval - lower 0.117
90 Percent confidence interval - upper 0.134
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.060
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_curr1 =~
b05_04_ (lmb1) 0.889 0.044 20.156 0.000
b05_09_ (lmb2) 0.720 0.045 15.945 0.000
b05_14_ (lmb3) -0.831 0.047 -17.825 0.000
b05_19_ (lmb4) -0.598 0.040 -14.906 0.000
b05_24_ (lmb5) 0.803 0.045 17.928 0.000
b05_29_ (lmb6) 0.903 0.042 21.305 0.000
b05_34_ (lmb7) -0.810 0.041 -19.588 0.000
b05_39_ (lmb8) -0.923 0.044 -20.954 0.000
b05_44_ (lmb9) 0.776 0.043 18.009 0.000
b05_49_ (lm10) 0.599 0.046 12.942 0.000
b05_54_ (lm11) -0.949 0.045 -21.283 0.000
b05_59_ (lm12) -0.759 0.048 -15.789 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_04_1 (i1) 2.754 0.048 57.017 0.000
.bf05_09_1 2.926 0.047 62.162 0.000
.bf05_14_1 3.257 0.050 65.636 0.000
.bf05_19_1 3.639 0.041 87.961 0.000
.bf05_24_1 2.942 0.048 61.537 0.000
.bf05_29_1 2.908 0.047 61.917 0.000
.bf05_34_1 3.861 0.045 85.935 0.000
.bf05_39_1 3.383 0.048 70.033 0.000
.bf05_44_1 3.161 0.046 69.232 0.000
.bf05_49_1 2.366 0.047 50.510 0.000
.bf05_54_1 3.331 0.049 67.698 0.000
.bf05_59_1 3.151 0.050 63.190 0.000
neur_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_04_t1 0.852 0.051 16.673 0.000
.bf05_09_t1 1.041 0.059 17.648 0.000
.bf05_14_t1 1.042 0.060 17.251 0.000
.bf05_19_t1 0.848 0.048 17.841 0.000
.bf05_24_t1 0.963 0.056 17.288 0.000
.bf05_29_t1 0.737 0.046 16.091 0.000
.bf05_34_t1 0.766 0.046 16.816 0.000
.bf05_39_t1 0.791 0.050 15.897 0.000
.bf05_44_t1 0.865 0.051 16.946 0.000
.bf05_49_t1 1.184 0.065 18.086 0.000
.bf05_54_t1 0.804 0.051 15.845 0.000
.bf05_59_t1 1.174 0.067 17.579 0.000
neuro_curr1 1.000 tidy(fit_cfa_neuro_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 neuro_curr1 =~ bf05_54_t1 =~ lambda11 -0.949 0.0446 -0.727
2 neuro_curr1 =~ bf05_39_t1 =~ lambda8 -0.923 0.0440 -0.720
3 neuro_curr1 =~ bf05_29_t1 =~ lambda6 0.903 0.0424 0.725
4 neuro_curr1 =~ bf05_04_t1 =~ lambda1 0.889 0.0441 0.694
5 neuro_curr1 =~ bf05_14_t1 =~ lambda3 -0.831 0.0466 -0.631
6 neuro_curr1 =~ bf05_34_t1 =~ lambda7 -0.810 0.0413 -0.679
7 neuro_curr1 =~ bf05_24_t1 =~ lambda5 0.803 0.0448 0.633
8 neuro_curr1 =~ bf05_44_t1 =~ lambda9 0.776 0.0431 0.641
9 neuro_curr1 =~ bf05_59_t1 =~ lambda12 -0.759 0.0481 -0.574
10 neuro_curr1 =~ bf05_09_t1 =~ lambda2 0.720 0.0451 0.577
11 neuro_curr1 =~ bf05_49_t1 =~ lambda10 0.599 0.0463 0.483
12 neuro_curr1 =~ bf05_19_t1 =~ lambda4 -0.598 0.0401 -0.545Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_29_t1_r = bf05_29_t1,
bf05_04_t1_r = bf05_04_t1,
bf05_24_t1_r = bf05_24_t1,
bf05_44_t1_r = bf05_44_t1,
bf05_09_t1_r = bf05_09_t1,
bf05_49_t1_r = bf05_49_t1,
bf05_29_t2_r = bf05_29_t2,
bf05_04_t2_r = bf05_04_t2,
bf05_24_t2_r = bf05_24_t2,
bf05_44_t2_r = bf05_44_t2,
bf05_09_t2_r = bf05_09_t2,
bf05_49_t2_r = bf05_49_t2) %>%
mutate(across(c(bf05_29_t1_r, bf05_04_t1_r, bf05_24_t1_r, bf05_44_t1_r, bf05_09_t1_r, bf05_49_t1_r,
bf05_29_t2_r, bf05_04_t2_r, bf05_24_t2_r, bf05_44_t2_r, bf05_09_t2_r, bf05_49_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(neuro_curr_par1_t1 = rowMeans(across(c(bf05_54_t1, bf05_34_t1, bf05_59_t1, bf05_19_t1)), na.rm=T),
neuro_curr_par2_t1 = rowMeans(across(c(bf05_39_t1, bf05_14_t1, bf05_44_t1_r, bf05_49_t1_r)), na.rm=T),
neuro_curr_par3_t1 = rowMeans(across(c(bf05_29_t1_r, bf05_04_t1_r, bf05_24_t1_r, bf05_09_t1_r)), na.rm=T),
neuro_curr_par1_t2 = rowMeans(across(c(bf05_54_t2, bf05_34_t2, bf05_59_t2, bf05_19_t2)), na.rm=T),
neuro_curr_par2_t2 = rowMeans(across(c(bf05_39_t2, bf05_14_t2, bf05_44_t2_r, bf05_49_t2_r)), na.rm=T),
neuro_curr_par3_t2 = rowMeans(across(c(bf05_29_t2_r, bf05_04_t2_r, bf05_24_t2_r, bf05_09_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*bf06_04_t1 + lambda1*bf06_04_t1 + lambda2*bf06_09_t1 + lambda3*bf06_14_t1 + lambda4*bf06_19_t1 + lambda5*bf06_24_t1 + lambda6*bf06_29_t1 + lambda7*bf06_34_t1 + lambda8*bf06_39_t1 + lambda9*bf06_44_t1 + lambda10*bf06_49_t1 + lambda11*bf06_54_t1 + lambda12*bf06_59_t1
# Intercepts
bf06_04_t1 ~ i1*1
bf06_09_t1 ~ 1
bf06_14_t1 ~ 1
bf06_19_t1 ~ 1
bf06_24_t1 ~ 1
bf06_29_t1 ~ 1
bf06_34_t1 ~ 1
bf06_39_t1 ~ 1
bf06_44_t1 ~ 1
bf06_49_t1 ~ 1
bf06_54_t1 ~ 1
bf06_59_t1 ~ 1
# Unique Variances
bf06_04_t1 ~~ bf06_04_t1
bf06_09_t1 ~~ bf06_09_t1
bf06_14_t1 ~~ bf06_14_t1
bf06_19_t1 ~~ bf06_19_t1
bf06_24_t1 ~~ bf06_24_t1
bf06_29_t1 ~~ bf06_29_t1
bf06_34_t1 ~~ bf06_34_t1
bf06_39_t1 ~~ bf06_39_t1
bf06_44_t1 ~~ bf06_44_t1
bf06_49_t1 ~~ bf06_49_t1
bf06_54_t1 ~~ bf06_54_t1
bf06_59_t1 ~~ bf06_59_t1
# Latent Variable Means
neuro_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
'
fit_cfa_neuro_ideal <- cfa(cfa_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_neuro_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 38 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 3
Model Test User Model:
Test statistic 168.864
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1410.123
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.915
Tucker-Lewis Index (TLI) 0.896
Robust Comparative Fit Index (CFI) 0.915
Robust Tucker-Lewis Index (TLI) 0.896
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10550.799
Loglikelihood unrestricted model (H1) -10466.367
Akaike (AIC) 21173.599
Bayesian (BIC) 21337.643
Sample-size adjusted Bayesian (SABIC) 21223.335
Root Mean Square Error of Approximation:
RMSEA 0.055
90 Percent confidence interval - lower 0.046
90 Percent confidence interval - upper 0.064
P-value H_0: RMSEA <= 0.050 0.182
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.055
90 Percent confidence interval - lower 0.046
90 Percent confidence interval - upper 0.064
P-value H_0: Robust RMSEA <= 0.050 0.182
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.042
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_ideal1 =~
b06_04_ (lmb1) 0.331 0.029 11.280 0.000
b06_09_ (lmb2) 0.348 0.033 10.653 0.000
b06_14_ (lmb3) -0.477 0.036 -13.201 0.000
b06_19_ (lmb4) -0.491 0.040 -12.203 0.000
b06_24_ (lmb5) 0.314 0.028 11.016 0.000
b06_29_ (lmb6) 0.355 0.043 8.242 0.000
b06_34_ (lmb7) -0.540 0.037 -14.464 0.000
b06_39_ (lmb8) -0.557 0.032 -17.368 0.000
b06_44_ (lmb9) 0.401 0.032 12.421 0.000
b06_49_ (lm10) 0.340 0.062 5.478 0.000
b06_54_ (lm11) -0.517 0.030 -17.380 0.000
b06_59_ (lm12) -0.432 0.037 -11.566 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_04_1 (i1) 4.554 0.028 164.475 0.000
.bf06_09_1 4.378 0.031 143.156 0.000
.bf06_14_1 1.719 0.035 49.385 0.000
.bf06_19_1 2.111 0.038 55.024 0.000
.bf06_24_1 4.639 0.027 173.498 0.000
.bf06_29_1 4.215 0.040 106.203 0.000
.bf06_34_1 1.868 0.036 51.236 0.000
.bf06_39_1 1.657 0.032 51.363 0.000
.bf06_44_1 4.376 0.031 141.928 0.000
.bf06_49_1 3.544 0.056 62.996 0.000
.bf06_54_1 1.501 0.030 50.163 0.000
.bf06_59_1 1.967 0.035 55.612 0.000
neuro_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_04_t1 0.430 0.025 17.523 0.000
.bf06_09_t1 0.537 0.030 17.658 0.000
.bf06_14_t1 0.625 0.037 17.054 0.000
.bf06_19_t1 0.795 0.046 17.321 0.000
.bf06_24_t1 0.405 0.023 17.566 0.000
.bf06_29_t1 0.981 0.054 18.131 0.000
.bf06_34_t1 0.644 0.039 16.636 0.000
.bf06_39_t1 0.421 0.028 15.220 0.000
.bf06_44_t1 0.508 0.029 17.257 0.000
.bf06_49_t1 2.113 0.114 18.505 0.000
.bf06_54_t1 0.363 0.024 15.250 0.000
.bf06_59_t1 0.694 0.040 17.483 0.000
neuro_ideal1 1.000 tidy(fit_cfa_neuro_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 neuro_ideal1 =~ bf06_39_t1 =~ lambda8 -0.557 0.0321 -0.652
2 neuro_ideal1 =~ bf06_34_t1 =~ lambda7 -0.540 0.0374 -0.559
3 neuro_ideal1 =~ bf06_54_t1 =~ lambda11 -0.517 0.0298 -0.651
4 neuro_ideal1 =~ bf06_19_t1 =~ lambda4 -0.491 0.0403 -0.483
5 neuro_ideal1 =~ bf06_14_t1 =~ lambda3 -0.477 0.0361 -0.517
6 neuro_ideal1 =~ bf06_59_t1 =~ lambda12 -0.432 0.0374 -0.460
7 neuro_ideal1 =~ bf06_44_t1 =~ lambda9 0.401 0.0323 0.491
8 neuro_ideal1 =~ bf06_29_t1 =~ lambda6 0.355 0.0431 0.338
9 neuro_ideal1 =~ bf06_09_t1 =~ lambda2 0.348 0.0327 0.429
10 neuro_ideal1 =~ bf06_49_t1 =~ lambda10 0.340 0.0621 0.228
11 neuro_ideal1 =~ bf06_04_t1 =~ lambda1 0.331 0.0294 0.451
12 neuro_ideal1 =~ bf06_24_t1 =~ lambda5 0.314 0.0285 0.442Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_29_t1_r = bf06_29_t1,
bf06_04_t1_r = bf06_04_t1,
bf06_24_t1_r = bf06_24_t1,
bf06_44_t1_r = bf06_44_t1,
bf06_09_t1_r = bf06_09_t1,
bf06_49_t1_r = bf06_49_t1,
bf06_29_t2_r = bf06_29_t2,
bf06_04_t2_r = bf06_04_t2,
bf06_24_t2_r = bf06_24_t2,
bf06_44_t2_r = bf06_44_t2,
bf06_09_t2_r = bf06_09_t2,
bf06_49_t2_r = bf06_49_t2) %>%
mutate(across(c(bf06_29_t1_r, bf06_04_t1_r, bf06_24_t1_r, bf06_44_t1_r, bf06_09_t1_r, bf06_49_t1_r,
bf06_29_t2_r, bf06_04_t2_r, bf06_24_t2_r, bf06_44_t2_r, bf06_09_t2_r, bf06_49_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(neuro_ideal_par1_t1 = rowMeans(across(c(bf06_39_t1, bf06_59_t1, bf06_09_t1_r, bf06_24_t1_r)), na.rm=T),
neuro_ideal_par2_t1 = rowMeans(across(c(bf06_34_t1, bf06_14_t1, bf06_29_t1_r, bf06_04_t1_r)), na.rm=T),
neuro_ideal_par3_t1 = rowMeans(across(c(bf06_54_t1, bf06_19_t1, bf06_44_t1_r, bf06_49_t1_r)), na.rm=T),
neuro_ideal_par1_t2 = rowMeans(across(c(bf06_39_t2, bf06_59_t2, bf06_09_t2_r, bf06_24_t2_r)), na.rm=T),
neuro_ideal_par2_t2 = rowMeans(across(c(bf06_34_t2, bf06_14_t2, bf06_29_t2_r, bf06_04_t2_r)), na.rm=T),
neuro_ideal_par3_t2 = rowMeans(across(c(bf06_54_t2, bf06_19_t2, bf06_44_t2_r, bf06_49_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*bf05_05_t1 + lambda1*bf05_05_t1 + lambda2*bf05_10_t1 + lambda3*bf05_15_t1 + lambda4*bf05_20_t1 + lambda5*bf05_25_t1 + lambda6*bf05_30_t1 + lambda7*bf05_35_t1 + lambda8*bf05_40_t1 + lambda9*bf05_45_t1 + lambda10*bf05_50_t1 + lambda11*bf05_55_t1 + lambda12*bf05_60_t1
# Intercepts
bf05_05_t1 ~ i1*1
bf05_10_t1 ~ 1
bf05_15_t1 ~ 1
bf05_20_t1 ~ 1
bf05_25_t1 ~ 1
bf05_30_t1 ~ 1
bf05_35_t1 ~ 1
bf05_40_t1 ~ 1
bf05_45_t1 ~ 1
bf05_50_t1 ~ 1
bf05_55_t1 ~ 1
bf05_60_t1 ~ 1
# Unique Variances
bf05_05_t1 ~~ bf05_05_t1
bf05_10_t1 ~~ bf05_10_t1
bf05_15_t1 ~~ bf05_15_t1
bf05_20_t1 ~~ bf05_20_t1
bf05_25_t1 ~~ bf05_25_t1
bf05_30_t1 ~~ bf05_30_t1
bf05_35_t1 ~~ bf05_35_t1
bf05_40_t1 ~~ bf05_40_t1
bf05_45_t1 ~~ bf05_45_t1
bf05_50_t1 ~~ bf05_50_t1
bf05_55_t1 ~~ bf05_55_t1
bf05_60_t1 ~~ bf05_60_t1
# Latent Variable Means
openn_curr1 ~ 0*1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
'
fit_cfa_openn_curr <- cfa(cfa_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_openn_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 42 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 5
Model Test User Model:
Test statistic 724.925
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1968.166
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.647
Tucker-Lewis Index (TLI) 0.569
Robust Comparative Fit Index (CFI) 0.647
Robust Tucker-Lewis Index (TLI) 0.569
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12474.714
Loglikelihood unrestricted model (H1) -12112.251
Akaike (AIC) 25021.427
Bayesian (BIC) 25185.471
Sample-size adjusted Bayesian (SABIC) 25071.164
Root Mean Square Error of Approximation:
RMSEA 0.133
90 Percent confidence interval - lower 0.124
90 Percent confidence interval - upper 0.142
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.133
90 Percent confidence interval - lower 0.124
90 Percent confidence interval - upper 0.142
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.083
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
openn_curr1 =~
b05_05_ (lmb1) 0.052 0.055 0.948 0.343
b05_10_ (lmb2) -0.532 0.040 -13.357 0.000
b05_15_ (lmb3) -0.466 0.044 -10.486 0.000
b05_20_ (lmb4) -0.754 0.050 -15.212 0.000
b05_25_ (lmb5) 0.511 0.053 9.667 0.000
b05_30_ (lmb6) 0.537 0.052 10.368 0.000
b05_35_ (lmb7) -0.700 0.044 -16.029 0.000
b05_40_ (lmb8) -0.389 0.040 -9.626 0.000
b05_45_ (lmb9) 0.528 0.047 11.165 0.000
b05_50_ (lm10) 0.673 0.055 12.224 0.000
b05_55_ (lm11) 0.593 0.046 12.769 0.000
b05_60_ (lm12) -0.560 0.044 -12.592 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_05_1 (i1) 3.024 0.049 61.271 0.000
.bf05_10_1 4.064 0.037 109.383 0.000
.bf05_15_1 3.588 0.038 94.497 0.000
.bf05_20_1 3.817 0.044 86.571 0.000
.bf05_25_1 2.420 0.048 49.998 0.000
.bf05_30_1 2.635 0.048 55.349 0.000
.bf05_35_1 3.888 0.040 98.118 0.000
.bf05_40_1 3.911 0.037 105.467 0.000
.bf05_45_1 2.075 0.044 47.261 0.000
.bf05_50_1 2.543 0.050 50.534 0.000
.bf05_55_1 2.543 0.044 57.452 0.000
.bf05_60_1 3.526 0.040 88.896 0.000
opnn_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_05_t1 1.708 0.091 18.728 0.000
.bf05_10_t1 0.689 0.042 16.217 0.000
.bf05_15_t1 0.798 0.049 16.286 0.000
.bf05_20_t1 0.800 0.062 12.821 0.000
.bf05_25_t1 1.389 0.078 17.726 0.000
.bf05_30_t1 1.304 0.074 17.513 0.000
.bf05_35_t1 0.615 0.049 12.502 0.000
.bf05_40_t1 0.816 0.046 17.744 0.000
.bf05_45_t1 1.076 0.062 17.311 0.000
.bf05_50_t1 1.330 0.081 16.493 0.000
.bf05_55_t1 1.027 0.060 17.041 0.000
.bf05_60_t1 0.794 0.051 15.646 0.000
openn_curr1 1.000 tidy(fit_cfa_openn_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 openn_curr1 =~ bf05_20_t1 =~ lambda4 -0.754 0.0495 -0.644
2 openn_curr1 =~ bf05_35_t1 =~ lambda7 -0.700 0.0437 -0.666
3 openn_curr1 =~ bf05_50_t1 =~ lambda10 0.673 0.0550 0.504
4 openn_curr1 =~ bf05_55_t1 =~ lambda11 0.593 0.0465 0.505
5 openn_curr1 =~ bf05_60_t1 =~ lambda12 -0.560 0.0445 -0.532
6 openn_curr1 =~ bf05_30_t1 =~ lambda6 0.537 0.0518 0.425
7 openn_curr1 =~ bf05_10_t1 =~ lambda2 -0.532 0.0398 -0.539
8 openn_curr1 =~ bf05_45_t1 =~ lambda9 0.528 0.0473 0.454
9 openn_curr1 =~ bf05_25_t1 =~ lambda5 0.511 0.0528 0.398
10 openn_curr1 =~ bf05_15_t1 =~ lambda3 -0.466 0.0444 -0.462
11 openn_curr1 =~ bf05_40_t1 =~ lambda8 -0.389 0.0404 -0.396
12 openn_curr1 =~ bf05_05_t1 =~ lambda1 0.0522 0.0551 0.0399Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_50_t1_r = bf05_50_t1,
bf05_55_t1_r = bf05_55_t1,
bf05_30_t1_r = bf05_30_t1,
bf05_25_t1_r = bf05_25_t1,
bf05_45_t1_r = bf05_45_t1,
bf05_05_t1_r = bf05_05_t1,
bf05_50_t2_r = bf05_50_t2,
bf05_55_t2_r = bf05_55_t2,
bf05_30_t2_r = bf05_30_t2,
bf05_25_t2_r = bf05_25_t2,
bf05_45_t2_r = bf05_45_t2,
bf05_05_t2_r = bf05_05_t2) %>%
mutate(across(c(bf05_50_t1_r, bf05_55_t1_r, bf05_30_t1_r, bf05_25_t1_r, bf05_45_t1_r, bf05_05_t1_r,
bf05_50_t2_r, bf05_55_t2_r, bf05_30_t2_r, bf05_25_t2_r, bf05_45_t2_r, bf05_05_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(openn_curr_par1_t1 = rowMeans(across(c(bf05_20_t1, bf05_30_t1_r, bf05_25_t1_r, bf05_05_t1_r)), na.rm=T),
openn_curr_par2_t1 = rowMeans(across(c(bf05_35_t1, bf05_60_t1, bf05_45_t1_r, bf05_40_t1)), na.rm=T),
openn_curr_par3_t1 = rowMeans(across(c(bf05_50_t1_r, bf05_55_t1_r, bf05_10_t1, bf05_15_t1)), na.rm=T),
openn_curr_par1_t2 = rowMeans(across(c(bf05_20_t2, bf05_30_t2_r, bf05_25_t2_r, bf05_05_t2_r)), na.rm=T),
openn_curr_par2_t2 = rowMeans(across(c(bf05_35_t2, bf05_60_t2, bf05_45_t2_r, bf05_40_t2)), na.rm=T),
openn_curr_par3_t2 = rowMeans(across(c(bf05_50_t2_r, bf05_55_t2_r, bf05_10_t2, bf05_15_t2)), na.rm=T))Check CFA item loadings at T1
cfa_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*bf06_05_t1 + lambda1*bf06_05_t1 + lambda2*bf06_10_t1 + lambda3*bf06_15_t1 + lambda4*bf06_20_t1 + lambda5*bf06_25_t1 + lambda6*bf06_30_t1 + lambda7*bf06_35_t1 + lambda8*bf06_40_t1 + lambda9*bf06_45_t1 + lambda10*bf06_50_t1 + lambda11*bf06_55_t1 + lambda12*bf06_60_t1
# Intercepts
bf06_05_t1 ~ i1*1
bf06_10_t1 ~ 1
bf06_15_t1 ~ 1
bf06_20_t1 ~ 1
bf06_25_t1 ~ 1
bf06_30_t1 ~ 1
bf06_35_t1 ~ 1
bf06_40_t1 ~ 1
bf06_45_t1 ~ 1
bf06_50_t1 ~ 1
bf06_55_t1 ~ 1
bf06_60_t1 ~ 1
# Unique Variances
bf06_05_t1 ~~ bf06_05_t1
bf06_10_t1 ~~ bf06_10_t1
bf06_15_t1 ~~ bf06_15_t1
bf06_20_t1 ~~ bf06_20_t1
bf06_25_t1 ~~ bf06_25_t1
bf06_30_t1 ~~ bf06_30_t1
bf06_35_t1 ~~ bf06_35_t1
bf06_40_t1 ~~ bf06_40_t1
bf06_45_t1 ~~ bf06_45_t1
bf06_50_t1 ~~ bf06_50_t1
bf06_55_t1 ~~ bf06_55_t1
bf06_60_t1 ~~ bf06_60_t1
# Latent Variable Means
openn_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
'
fit_cfa_openn_ideal <- cfa(cfa_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_openn_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 48 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 3
Model Test User Model:
Test statistic 365.801
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1468.753
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.778
Tucker-Lewis Index (TLI) 0.728
Robust Comparative Fit Index (CFI) 0.777
Robust Tucker-Lewis Index (TLI) 0.728
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11578.781
Loglikelihood unrestricted model (H1) -11395.881
Akaike (AIC) 23229.562
Bayesian (BIC) 23393.606
Sample-size adjusted Bayesian (SABIC) 23279.299
Root Mean Square Error of Approximation:
RMSEA 0.091
90 Percent confidence interval - lower 0.082
90 Percent confidence interval - upper 0.099
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.977
Robust RMSEA 0.091
90 Percent confidence interval - lower 0.082
90 Percent confidence interval - upper 0.100
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.978
Standardized Root Mean Square Residual:
SRMR 0.061
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
openn_ideal1 =~
b06_05_ (lmb1) 0.000 0.063 0.007 0.994
b06_10_ (lmb2) -0.367 0.031 -11.834 0.000
b06_15_ (lmb3) -0.332 0.027 -12.436 0.000
b06_20_ (lmb4) -0.640 0.037 -17.212 0.000
b06_25_ (lmb5) 0.505 0.046 10.924 0.000
b06_30_ (lmb6) 0.501 0.058 8.603 0.000
b06_35_ (lmb7) -0.629 0.035 -18.181 0.000
b06_40_ (lmb8) -0.367 0.045 -8.209 0.000
b06_45_ (lmb9) 0.384 0.043 8.974 0.000
b06_50_ (lm10) 0.549 0.047 11.700 0.000
b06_55_ (lm11) 0.436 0.051 8.485 0.000
b06_60_ (lm12) -0.394 0.030 -13.291 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_05_1 (i1) 3.205 0.056 57.477 0.000
.bf06_10_1 4.452 0.029 153.591 0.000
.bf06_15_1 4.588 0.025 186.317 0.000
.bf06_20_1 4.284 0.036 118.738 0.000
.bf06_25_1 1.908 0.043 44.545 0.000
.bf06_30_1 2.027 0.053 38.141 0.000
.bf06_35_1 4.266 0.034 125.636 0.000
.bf06_40_1 3.923 0.041 96.111 0.000
.bf06_45_1 1.667 0.039 42.796 0.000
.bf06_50_1 2.105 0.044 47.826 0.000
.bf06_55_1 2.232 0.047 47.600 0.000
.bf06_60_1 4.524 0.028 162.198 0.000
openn_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_05_t1 2.186 0.117 18.748 0.000
.bf06_10_t1 0.457 0.027 17.164 0.000
.bf06_15_t1 0.317 0.019 16.579 0.000
.bf06_20_t1 0.507 0.037 13.847 0.000
.bf06_25_t1 1.036 0.060 17.393 0.000
.bf06_30_t1 1.736 0.097 17.984 0.000
.bf06_35_t1 0.416 0.032 13.100 0.000
.bf06_40_t1 1.039 0.057 18.100 0.000
.bf06_45_t1 0.920 0.051 17.859 0.000
.bf06_50_t1 1.062 0.062 17.267 0.000
.bf06_55_t1 1.358 0.075 18.030 0.000
.bf06_60_t1 0.393 0.024 16.465 0.000
openn_ideal1 1.000 tidy(fit_cfa_openn_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 openn_ideal1 =~ bf06_20_t1 =~ lambda4 -0.640 0.0372 -0.669
2 openn_ideal1 =~ bf06_35_t1 =~ lambda7 -0.629 0.0346 -0.698
3 openn_ideal1 =~ bf06_50_t1 =~ lambda10 0.549 0.0469 0.470
4 openn_ideal1 =~ bf06_25_t1 =~ lambda5 0.505 0.0463 0.445
5 openn_ideal1 =~ bf06_30_t1 =~ lambda6 0.501 0.0582 0.355
6 openn_ideal1 =~ bf06_55_t1 =~ lambda11 0.436 0.0513 0.350
7 openn_ideal1 =~ bf06_60_t1 =~ lambda12 -0.394 0.0296 -0.532
8 openn_ideal1 =~ bf06_45_t1 =~ lambda9 0.384 0.0428 0.371
9 openn_ideal1 =~ bf06_40_t1 =~ lambda8 -0.367 0.0447 -0.339
10 openn_ideal1 =~ bf06_10_t1 =~ lambda2 -0.367 0.0310 -0.477
11 openn_ideal1 =~ bf06_15_t1 =~ lambda3 -0.332 0.0267 -0.507
12 openn_ideal1 =~ bf06_05_t1 =~ lambda1 0.000466 0.0628 0.000315Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_50_t1_r = bf06_50_t1,
bf06_55_t1_r = bf06_55_t1,
bf06_30_t1_r = bf06_30_t1,
bf06_25_t1_r = bf06_25_t1,
bf06_45_t1_r = bf06_45_t1,
bf06_05_t1_r = bf06_05_t1,
bf06_50_t2_r = bf06_50_t2,
bf06_55_t2_r = bf06_55_t2,
bf06_30_t2_r = bf06_30_t2,
bf06_25_t2_r = bf06_25_t2,
bf06_45_t2_r = bf06_45_t2,
bf06_05_t2_r = bf06_05_t2) %>%
mutate(across(c(bf06_50_t1_r, bf06_55_t1_r, bf06_30_t1_r, bf06_25_t1_r, bf06_45_t1_r, bf06_05_t1_r,
bf06_50_t2_r, bf06_55_t2_r, bf06_30_t2_r, bf06_25_t2_r, bf06_45_t2_r, bf06_05_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(openn_ideal_par1_t1 = rowMeans(across(c(bf06_20_t1, bf06_55_t1_r, bf06_40_t1, bf06_05_t1)), na.rm=T),
openn_ideal_par2_t1 = rowMeans(across(c(bf06_35_t1, bf06_30_t1_r, bf06_45_t1_r, bf06_15_t1)), na.rm=T),
openn_ideal_par3_t1 = rowMeans(across(c(bf06_50_t1_r, bf06_25_t1_r, bf06_60_t1, bf06_10_t1)), na.rm=T),
openn_ideal_par1_t2 = rowMeans(across(c(bf06_20_t2, bf06_55_t2_r, bf06_40_t2, bf06_05_t2)), na.rm=T),
openn_ideal_par2_t2 = rowMeans(across(c(bf06_35_t2, bf06_30_t2_r, bf06_45_t2_r, bf06_15_t2)), na.rm=T),
openn_ideal_par3_t2 = rowMeans(across(c(bf06_50_t2_r, bf06_25_t2_r, bf06_60_t2, bf06_10_t2)), na.rm=T))
# replace NaN with regular NA
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate_all(~ifelse(is.nan(.), NA, .))Testing for measurement invariance of the latent traits across time.
Satisfaction with life scale (only the first four items - last one taps into a somewhat different construct)
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + sw06_02_t1 + sw06_03_t1 + sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + sw06_02_t2 + sw06_03_t2 + sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ 1
sw06_03_t1 ~ 1
sw06_04_t1 ~ 1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ 1
sw06_03_t2 ~ 1
sw06_04_t2 ~ 1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_configural_swls <- cfa(configural_swls, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_swls, fit.measures = TRUE)
# Weak invariance model
weak_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ 1
sw06_03_t1 ~ 1
sw06_04_t1 ~ 1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ 1
sw06_03_t2 ~ 1
sw06_04_t2 ~ 1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_weak_swls <- cfa(weak_swls, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_swls, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ i2*1
sw06_03_t1 ~ i3*1
sw06_04_t1 ~ i4*1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ i2*1
sw06_03_t2 ~ i3*1
sw06_04_t2 ~ i4*1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_strong_swls <- cfa(strong_swls, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_swls, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ i2*1
sw06_03_t1 ~ i3*1
sw06_04_t1 ~ i4*1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ i2*1
sw06_03_t2 ~ i3*1
sw06_04_t2 ~ i4*1
# Unique Variances
sw06_01_t1 ~~ u1*sw06_01_t1
sw06_02_t1 ~~ u2*sw06_02_t1
sw06_03_t1 ~~ u3*sw06_03_t1
sw06_04_t1 ~~ u4*sw06_04_t1
sw06_01_t2 ~~ u1*sw06_01_t2
sw06_02_t2 ~~ u2*sw06_02_t2
sw06_03_t2 ~~ u3*sw06_03_t2
sw06_04_t2 ~~ u4*sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_strict_swls <- cfa(strict_swls, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_swls, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 714 25 288. 14504. 14618. 0.912 0.870 0.141 0.0476
2 weak 714 22 289. 14499. 14599. 0.912 0.888 0.130 0.0484
3 strong 714 19 291. 14495. 14582. 0.913 0.902 0.122 0.0483
4 strict 714 15 303. 14498. 14567. 0.910 0.913 0.115 0.0511# chi-square difference test for nested models
anova(fit_configural_swls, fit_weak_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_configural_swls 19 14504 14618 288.29
fit_weak_swls 22 14499 14599 289.34 1.046 0 3 0.7901anova(fit_weak_swls, fit_strong_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_swls 22 14499 14599 289.34
fit_strong_swls 25 14495 14582 291.26 1.9206 0 3 0.5891anova(fit_strong_swls, fit_strict_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_swls 25 14495 14582 291.26
fit_strict_swls 29 14498 14567 302.77 11.516 0.051301 4 0.02133 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + meaning_par2_t1 + meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + meaning_par2_t2 + meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ 1
meaning_par3_t1 ~ 1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ 1
meaning_par3_t2 ~ 1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_configural_meaning <- cfa(configural_meaning, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_meaning, fit.measures = TRUE)
# Weak invariance model
weak_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ 1
meaning_par3_t1 ~ 1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ 1
meaning_par3_t2 ~ 1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_weak_meaning <- cfa(weak_meaning, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_meaning, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ i2*1
meaning_par3_t1 ~ i3*1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ i2*1
meaning_par3_t2 ~ i3*1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_strong_meaning <- cfa(strong_meaning, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_meaning, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ i2*1
meaning_par3_t1 ~ i3*1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ i2*1
meaning_par3_t2 ~ i3*1
# Unique Variances
meaning_par1_t1 ~~ u1*meaning_par1_t1
meaning_par2_t1 ~~ u2*meaning_par2_t1
meaning_par3_t1 ~~ u3*meaning_par3_t1
meaning_par1_t2 ~~ u1*meaning_par1_t2
meaning_par2_t2 ~~ u2*meaning_par2_t2
meaning_par3_t2 ~~ u3*meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_strict_meaning <- cfa(strict_meaning, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_meaning, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 714 19 268. 10703. 10790. 0.911 0.834 0.213 0.0440
2 weak 714 17 268. 10700. 10777. 0.912 0.868 0.190 0.0442
3 strong 714 15 269. 10696. 10765. 0.912 0.891 0.173 0.0444
4 strict 714 12 276. 10698. 10752. 0.911 0.911 0.156 0.0488# chi-square difference test for nested models
anova(fit_configural_meaning, fit_weak_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_meaning 8 10703 10790 268.30
fit_weak_meaning 10 10700 10777 268.47 0.17055 0 2
Pr(>Chisq)
fit_configural_meaning
fit_weak_meaning 0.9183anova(fit_weak_meaning, fit_strong_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_meaning 10 10700 10777 268.47
fit_strong_meaning 12 10696 10765 268.98 0.5041 0 2 0.7772anova(fit_strong_meaning, fit_strict_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_meaning 12 10696 10765 268.98
fit_strict_meaning 15 10698 10752 276.46 7.4836 0.045751 3 0.05798
fit_strong_meaning
fit_strict_meaning .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + selfes_par2_t1 + selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + selfes_par2_t2 + selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ 1
selfes_par3_t1 ~ 1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ 1
selfes_par3_t2 ~ 1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_configural_selfes <- cfa(configural_selfes, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_selfes, fit.measures = TRUE)
# Weak invariance model
weak_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ 1
selfes_par3_t1 ~ 1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ 1
selfes_par3_t2 ~ 1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_weak_selfes <- cfa(weak_selfes, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_selfes, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ i2*1
selfes_par3_t1 ~ i3*1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ i2*1
selfes_par3_t2 ~ i3*1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_strong_selfes <- cfa(strong_selfes, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_selfes, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ i2*1
selfes_par3_t1 ~ i3*1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ i2*1
selfes_par3_t2 ~ i3*1
# Unique Variances
selfes_par1_t1 ~~ u1*selfes_par1_t1
selfes_par2_t1 ~~ u2*selfes_par2_t1
selfes_par3_t1 ~~ u3*selfes_par3_t1
selfes_par1_t2 ~~ u1*selfes_par1_t2
selfes_par2_t2 ~~ u2*selfes_par2_t2
selfes_par3_t2 ~~ u3*selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_strict_selfes <- cfa(strict_selfes, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_selfes, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 714 19 143. 7479. 7566. 0.964 0.932 0.154 0.0200
2 weak 714 17 143. 7476. 7554. 0.964 0.946 0.137 0.0226
3 strong 714 15 144. 7472. 7541. 0.965 0.956 0.124 0.0234
4 strict 714 12 146. 7469. 7523. 0.965 0.965 0.110 0.0215# chi-square difference test for nested models
anova(fit_configural_selfes, fit_weak_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_selfes 8 7479.4 7566.2 142.62
fit_weak_selfes 10 7476.2 7553.9 143.38 0.75476 0 2
Pr(>Chisq)
fit_configural_selfes
fit_weak_selfes 0.6857anova(fit_weak_selfes, fit_strong_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_selfes 10 7476.2 7553.9 143.38
fit_strong_selfes 12 7472.4 7541.0 143.66 0.2845 0 2 0.8674anova(fit_strong_selfes, fit_strict_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_selfes 12 7472.4 7541.0 143.66
fit_strict_selfes 15 7468.5 7523.4 145.76 2.1019 0 3 0.5515Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + concept_par2_t1 + concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + concept_par2_t2 + concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ 1
concept_par3_t1 ~ 1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ 1
concept_par3_t2 ~ 1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_configural_concept <- cfa(configural_concept, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_concept, fit.measures = TRUE)
# Weak invariance model
weak_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ 1
concept_par3_t1 ~ 1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ 1
concept_par3_t2 ~ 1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_weak_concept <- cfa(weak_concept, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_concept, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ i2*1
concept_par3_t1 ~ i3*1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ i2*1
concept_par3_t2 ~ i3*1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_strong_concept <- cfa(strong_concept, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_concept, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ i2*1
concept_par3_t1 ~ i3*1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ i2*1
concept_par3_t2 ~ i3*1
# Unique Variances
concept_par1_t1 ~~ u1*concept_par1_t1
concept_par2_t1 ~~ u2*concept_par2_t1
concept_par3_t1 ~~ u3*concept_par3_t1
concept_par1_t2 ~~ u1*concept_par1_t2
concept_par2_t2 ~~ u2*concept_par2_t2
concept_par3_t2 ~~ u3*concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_strict_concept <- cfa(strict_concept, data = df_sbsa_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_concept, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 714 19 117. 8469. 8556. 0.959 0.923 0.138 0.0248
2 weak 714 17 121. 8469. 8547. 0.958 0.937 0.125 0.0339
3 strong 714 15 122. 8466. 8535. 0.959 0.948 0.113 0.0319
4 strict 714 12 125. 8463. 8518. 0.959 0.959 0.101 0.0357# chi-square difference test for nested models
anova(fit_configural_concept, fit_weak_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_concept 8 8469.5 8556.3 117.30
fit_weak_concept 10 8469.5 8547.2 121.31 4.0127 0.037543 2
Pr(>Chisq)
fit_configural_concept
fit_weak_concept 0.1345anova(fit_weak_concept, fit_strong_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_concept 10 8469.5 8547.2 121.31
fit_strong_concept 12 8466.5 8535.0 122.31 0.99821 0 2 0.6071anova(fit_strong_concept, fit_strict_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_concept 12 8466.5 8535.0 122.31
fit_strict_concept 15 8463.0 8517.9 124.87 2.5587 0 3 0.4648Testing for measurement invariance of the latent BFI-2 traits across time.
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + extra_curr_par2_t1 + extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + extra_curr_par2_t2 + extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ 1
extra_curr_par3_t1 ~ 1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_configural_extra_curr <- cfa(configural_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_extra_curr, fit.measures = TRUE)
# Weak invariance model
weak_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ 1
extra_curr_par3_t1 ~ 1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_weak_extra_curr <- cfa(weak_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_extra_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ i2*1
extra_curr_par3_t1 ~ i3*1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strong_extra_curr <- cfa(strong_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_extra_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ i2*1
extra_curr_par3_t1 ~ i3*1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
# Unique Variances
extra_curr_par1_t1 ~~ u1*extra_curr_par1_t1
extra_curr_par2_t1 ~~ u2*extra_curr_par2_t1
extra_curr_par3_t1 ~~ u3*extra_curr_par3_t1
extra_curr_par1_t2 ~~ u1*extra_curr_par1_t2
extra_curr_par2_t2 ~~ u2*extra_curr_par2_t2
extra_curr_par3_t2 ~~ u3*extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strict_extra_curr <- cfa(strict_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_extra_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 357. 7780. 7867. 0.862 0.740 0.247 0.0524
2 weak 715 17 358. 7777. 7855. 0.862 0.793 0.221 0.0540
3 strong 715 15 359. 7774. 7842. 0.862 0.828 0.201 0.0552
4 strict 715 12 360. 7769. 7824. 0.863 0.863 0.179 0.0547# chi-square difference test for nested models
anova(fit_configural_extra_curr, fit_weak_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_extra_curr 8 7779.9 7866.8 356.95
fit_weak_extra_curr 10 7776.8 7854.5 357.80 0.84925 0 2
Pr(>Chisq)
fit_configural_extra_curr
fit_weak_extra_curr 0.654anova(fit_weak_extra_curr, fit_strong_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_extra_curr 10 7776.8 7854.5 357.8
fit_strong_extra_curr 12 7773.7 7842.3 358.7 0.893 0 2
Pr(>Chisq)
fit_weak_extra_curr
fit_strong_extra_curr 0.6399anova(fit_strong_extra_curr, fit_strict_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_extra_curr 12 7773.7 7842.3 358.69
fit_strict_extra_curr 15 7769.1 7823.9 360.07 1.3793 0 3
Pr(>Chisq)
fit_strong_extra_curr
fit_strict_extra_curr 0.7104Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + extra_ideal_par2_t1 + extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + extra_ideal_par2_t2 + extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ 1
extra_ideal_par3_t1 ~ 1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_configural_extra_ideal <- cfa(configural_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_extra_ideal, fit.measures = TRUE)
# Weak invariance model
weak_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ 1
extra_ideal_par3_t1 ~ 1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_weak_extra_ideal <- cfa(weak_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_extra_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ i2*1
extra_ideal_par3_t1 ~ i3*1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strong_extra_ideal <- cfa(strong_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_extra_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ i2*1
extra_ideal_par3_t1 ~ i3*1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
# Unique Variances
extra_ideal_par1_t1 ~~ u1*extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ u2*extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ u3*extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ u1*extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ u2*extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ u3*extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strict_extra_ideal <- cfa(strict_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_extra_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 146. 6689. 6776. 0.884 0.782 0.156 0.0514
2 weak 715 17 147. 6685. 6763. 0.885 0.828 0.138 0.0514
3 strong 715 15 153. 6688. 6756. 0.882 0.852 0.128 0.0541
4 strict 715 12 156. 6684. 6739. 0.882 0.882 0.115 0.0734# chi-square difference test for nested models
anova(fit_configural_extra_ideal, fit_weak_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_extra_ideal 8 6689.3 6776.2 146.47
fit_weak_extra_ideal 10 6685.4 6763.1 146.52 0.045527 0 2
Pr(>Chisq)
fit_configural_extra_ideal
fit_weak_extra_ideal 0.9775anova(fit_weak_extra_ideal, fit_strong_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_extra_ideal 10 6685.4 6763.1 146.52
fit_strong_extra_ideal 12 6687.8 6756.4 152.96 6.4435 0.055743 2
Pr(>Chisq)
fit_weak_extra_ideal
fit_strong_extra_ideal 0.03989 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_strong_extra_ideal, fit_strict_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_extra_ideal 12 6687.8 6756.4 152.96
fit_strict_extra_ideal 15 6684.5 6739.3 155.63 2.6654 0 3
Pr(>Chisq)
fit_strong_extra_ideal
fit_strict_extra_ideal 0.4461Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + agree_curr_par2_t1 + agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + agree_curr_par2_t2 + agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ 1
agree_curr_par3_t1 ~ 1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_configural_agree_curr <- cfa(configural_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_agree_curr, fit.measures = TRUE)
# Weak invariance model
weak_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ 1
agree_curr_par3_t1 ~ 1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_weak_agree_curr <- cfa(weak_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_agree_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ i2*1
agree_curr_par3_t1 ~ i3*1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strong_agree_curr <- cfa(strong_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_agree_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ i2*1
agree_curr_par3_t1 ~ i3*1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
# Unique Variances
agree_curr_par1_t1 ~~ u1*agree_curr_par1_t1
agree_curr_par2_t1 ~~ u2*agree_curr_par2_t1
agree_curr_par3_t1 ~~ u3*agree_curr_par3_t1
agree_curr_par1_t2 ~~ u1*agree_curr_par1_t2
agree_curr_par2_t2 ~~ u2*agree_curr_par2_t2
agree_curr_par3_t2 ~~ u3*agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strict_agree_curr <- cfa(strict_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_agree_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 202. 6509. 6596. 0.885 0.784 0.184 0.0541
2 weak 715 17 204. 6507. 6584. 0.885 0.828 0.165 0.0606
3 strong 715 15 205. 6504. 6572. 0.886 0.857 0.150 0.0590
4 strict 715 12 219. 6512. 6567. 0.879 0.879 0.138 0.0821# chi-square difference test for nested models
anova(fit_configural_agree_curr, fit_weak_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_agree_curr 8 6509.1 6595.9 202.19
fit_weak_agree_curr 10 6506.7 6584.4 203.84 1.6455 0 2
Pr(>Chisq)
fit_configural_agree_curr
fit_weak_agree_curr 0.4392anova(fit_weak_agree_curr, fit_strong_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_agree_curr 10 6506.7 6584.4 203.84
fit_strong_agree_curr 12 6503.9 6572.5 205.03 1.1924 0 2
Pr(>Chisq)
fit_weak_agree_curr
fit_strong_agree_curr 0.5509anova(fit_strong_agree_curr, fit_strict_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_agree_curr 12 6503.9 6572.5 205.03
fit_strict_agree_curr 15 6511.6 6566.5 218.76 13.733 0.070738 3
Pr(>Chisq)
fit_strong_agree_curr
fit_strict_agree_curr 0.003292 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + agree_ideal_par2_t1 + agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + agree_ideal_par2_t2 + agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ 1
agree_ideal_par3_t1 ~ 1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_configural_agree_ideal <- cfa(configural_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_agree_ideal, fit.measures = TRUE)
# Weak invariance model
weak_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ 1
agree_ideal_par3_t1 ~ 1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_weak_agree_ideal <- cfa(weak_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_agree_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ i2*1
agree_ideal_par3_t1 ~ i3*1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strong_agree_ideal <- cfa(strong_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_agree_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ i2*1
agree_ideal_par3_t1 ~ i3*1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
# Unique Variances
agree_ideal_par1_t1 ~~ u1*agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ u2*agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ u3*agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ u1*agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ u2*agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ u3*agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strict_agree_ideal <- cfa(strict_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_agree_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 163. 6608. 6695. 0.914 0.838 0.165 0.0504
2 weak 715 17 164. 6604. 6682. 0.915 0.872 0.147 0.0511
3 strong 715 15 166. 6602. 6671. 0.915 0.893 0.134 0.0506
4 strict 715 12 168. 6599. 6653. 0.915 0.915 0.119 0.0548# chi-square difference test for nested models
anova(fit_configural_agree_ideal, fit_weak_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_agree_ideal 8 6608.0 6694.8 163.41
fit_weak_agree_ideal 10 6604.1 6681.8 163.55 0.13458 0 2
Pr(>Chisq)
fit_configural_agree_ideal
fit_weak_agree_ideal 0.9349anova(fit_weak_agree_ideal, fit_strong_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_agree_ideal 10 6604.1 6681.8 163.55
fit_strong_agree_ideal 12 6602.3 6670.9 165.73 2.1834 0.011324 2
Pr(>Chisq)
fit_weak_agree_ideal
fit_strong_agree_ideal 0.3356anova(fit_strong_agree_ideal, fit_strict_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_agree_ideal 12 6602.3 6670.9 165.73
fit_strict_agree_ideal 15 6598.5 6653.4 167.98 2.2504 0 3
Pr(>Chisq)
fit_strong_agree_ideal
fit_strict_agree_ideal 0.5221Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + consc_curr_par2_t1 + consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + consc_curr_par2_t2 + consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ 1
consc_curr_par3_t1 ~ 1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_configural_consc_curr <- cfa(configural_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_consc_curr, fit.measures = TRUE)
# Weak invariance model
weak_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ 1
consc_curr_par3_t1 ~ 1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_weak_consc_curr <- cfa(weak_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_consc_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ i2*1
consc_curr_par3_t1 ~ i3*1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strong_consc_curr <- cfa(strong_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_consc_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ i2*1
consc_curr_par3_t1 ~ i3*1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
# Unique Variances
consc_curr_par1_t1 ~~ u1*consc_curr_par1_t1
consc_curr_par2_t1 ~~ u2*consc_curr_par2_t1
consc_curr_par3_t1 ~~ u3*consc_curr_par3_t1
consc_curr_par1_t2 ~~ u1*consc_curr_par1_t2
consc_curr_par2_t2 ~~ u2*consc_curr_par2_t2
consc_curr_par3_t2 ~~ u3*consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strict_consc_curr <- cfa(strict_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_consc_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 298. 7123. 7210. 0.916 0.843 0.225 0.0345
2 weak 715 17 299. 7121. 7198. 0.917 0.875 0.201 0.0371
3 strong 715 15 305. 7122. 7191. 0.916 0.894 0.185 0.0392
4 strict 715 12 315. 7126. 7181. 0.914 0.914 0.167 0.0446# chi-square difference test for nested models
anova(fit_configural_consc_curr, fit_weak_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_consc_curr 8 7123.3 7210.2 297.84
fit_weak_consc_curr 10 7120.6 7198.3 299.04 1.2051 0 2
Pr(>Chisq)
fit_configural_consc_curr
fit_weak_consc_curr 0.5474anova(fit_weak_consc_curr, fit_strong_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_consc_curr 10 7120.6 7198.3 299.04
fit_strong_consc_curr 12 7122.2 7190.8 304.72 5.6787 0.05072 2
Pr(>Chisq)
fit_weak_consc_curr
fit_strong_consc_curr 0.05846 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_strong_consc_curr, fit_strict_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_consc_curr 12 7122.2 7190.8 304.72
fit_strict_consc_curr 15 7126.0 7180.9 314.53 9.8061 0.05633 3
Pr(>Chisq)
fit_strong_consc_curr
fit_strict_consc_curr 0.02029 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + consc_ideal_par2_t1 + consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + consc_ideal_par2_t2 + consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ 1
consc_ideal_par3_t1 ~ 1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_configural_consc_ideal <- cfa(configural_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_consc_ideal, fit.measures = TRUE)
# Weak invariance model
weak_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ 1
consc_ideal_par3_t1 ~ 1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_weak_consc_ideal <- cfa(weak_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_consc_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ i2*1
consc_ideal_par3_t1 ~ i3*1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strong_consc_ideal <- cfa(strong_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_consc_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ i2*1
consc_ideal_par3_t1 ~ i3*1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
# Unique Variances
consc_ideal_par1_t1 ~~ u1*consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ u2*consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ u3*consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ u1*consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ u2*consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ u3*consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strict_consc_ideal <- cfa(strict_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_consc_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 64.9 5583. 5669. 0.965 0.934 0.0997 0.0256
2 weak 715 17 64.9 5579. 5656. 0.966 0.949 0.0876 0.0257
3 strong 715 15 65.2 5575. 5643. 0.967 0.959 0.0787 0.0259
4 strict 715 12 80.3 5584. 5639. 0.959 0.959 0.0780 0.0846# chi-square difference test for nested models
anova(fit_configural_consc_ideal, fit_weak_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_consc_ideal 8 5582.6 5669.4 64.866
fit_weak_consc_ideal 10 5578.6 5656.3 64.878 0.01134 0 2
Pr(>Chisq)
fit_configural_consc_ideal
fit_weak_consc_ideal 0.9943anova(fit_weak_consc_ideal, fit_strong_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_consc_ideal 10 5578.6 5656.3 64.878
fit_strong_consc_ideal 12 5574.9 5643.5 65.187 0.309 0 2
Pr(>Chisq)
fit_weak_consc_ideal
fit_strong_consc_ideal 0.8568anova(fit_strong_consc_ideal, fit_strict_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_consc_ideal 12 5574.9 5643.5 65.187
fit_strict_consc_ideal 15 5584.0 5638.9 80.313 15.126 0.075188 3
Pr(>Chisq)
fit_strong_consc_ideal
fit_strict_consc_ideal 0.001712 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + neuro_curr_par2_t1 + neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + neuro_curr_par2_t2 + neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ 1
neuro_curr_par3_t1 ~ 1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_configural_neuro_curr <- cfa(configural_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_neuro_curr, fit.measures = TRUE)
# Weak invariance model
weak_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ 1
neuro_curr_par3_t1 ~ 1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_weak_neuro_curr <- cfa(weak_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_neuro_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ i2*1
neuro_curr_par3_t1 ~ i3*1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strong_neuro_curr <- cfa(strong_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_neuro_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ i2*1
neuro_curr_par3_t1 ~ i3*1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
# Unique Variances
neuro_curr_par1_t1 ~~ u1*neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ u2*neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ u3*neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ u1*neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ u2*neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ u3*neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strict_neuro_curr <- cfa(strict_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_neuro_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 242. 7956. 8043. 0.929 0.867 0.202 0.0354
2 weak 715 17 244. 7953. 8031. 0.929 0.894 0.181 0.0380
3 strong 715 15 246. 7952. 8020. 0.929 0.912 0.165 0.0368
4 strict 715 12 247. 7947. 8001. 0.930 0.930 0.147 0.0381# chi-square difference test for nested models
anova(fit_configural_neuro_curr, fit_weak_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_neuro_curr 8 7955.8 8042.7 242.32
fit_weak_neuro_curr 10 7953.0 8030.8 243.54 1.2213 0 2
Pr(>Chisq)
fit_configural_neuro_curr
fit_weak_neuro_curr 0.543anova(fit_weak_neuro_curr, fit_strong_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_neuro_curr 10 7953.0 8030.8 243.54
fit_strong_neuro_curr 12 7951.9 8020.5 246.41 2.8662 0.024611 2
Pr(>Chisq)
fit_weak_neuro_curr
fit_strong_neuro_curr 0.2386anova(fit_strong_neuro_curr, fit_strict_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_neuro_curr 12 7951.9 8020.5 246.41
fit_strict_neuro_curr 15 7946.5 8001.4 247.04 0.63411 0 3
Pr(>Chisq)
fit_strong_neuro_curr
fit_strict_neuro_curr 0.8886Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + neuro_ideal_par2_t1 + neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + neuro_ideal_par2_t2 + neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ 1
neuro_ideal_par3_t1 ~ 1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_configural_neuro_ideal <- cfa(configural_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_neuro_ideal, fit.measures = TRUE)
# Weak invariance model
weak_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ 1
neuro_ideal_par3_t1 ~ 1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_weak_neuro_ideal <- cfa(weak_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_neuro_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ i2*1
neuro_ideal_par3_t1 ~ i3*1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strong_neuro_ideal <- cfa(strong_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_neuro_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ i2*1
neuro_ideal_par3_t1 ~ i3*1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
# Unique Variances
neuro_ideal_par1_t1 ~~ u1*neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ u2*neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ u3*neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ u1*neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ u2*neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ u3*neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strict_neuro_ideal <- cfa(strict_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_neuro_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 39.7 5816. 5903. 0.979 0.961 0.0745 0.0217
2 weak 715 17 41.2 5814. 5891. 0.979 0.969 0.0661 0.0261
3 strong 715 15 41.8 5810. 5879. 0.980 0.975 0.0589 0.0252
4 strict 715 12 43.9 5806. 5861. 0.981 0.981 0.0519 0.0358# chi-square difference test for nested models
anova(fit_configural_neuro_ideal, fit_weak_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_neuro_ideal 8 5816.1 5903.0 39.709
fit_weak_neuro_ideal 10 5813.6 5891.3 41.207 1.4977 0 2
Pr(>Chisq)
fit_configural_neuro_ideal
fit_weak_neuro_ideal 0.4729anova(fit_weak_neuro_ideal, fit_strong_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_neuro_ideal 10 5813.6 5891.3 41.207
fit_strong_neuro_ideal 12 5810.2 5878.8 41.777 0.57064 0 2
Pr(>Chisq)
fit_weak_neuro_ideal
fit_strong_neuro_ideal 0.7518anova(fit_strong_neuro_ideal, fit_strict_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_neuro_ideal 12 5810.2 5878.8 41.777
fit_strict_neuro_ideal 15 5806.3 5861.2 43.907 2.1301 0 3
Pr(>Chisq)
fit_strong_neuro_ideal
fit_strict_neuro_ideal 0.5459Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + openn_curr_par2_t1 + openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + openn_curr_par2_t2 + openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ 1
openn_curr_par3_t1 ~ 1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_configural_openn_curr <- cfa(configural_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_openn_curr, fit.measures = TRUE)
# Weak invariance model
weak_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ 1
openn_curr_par3_t1 ~ 1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_weak_openn_curr <- cfa(weak_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_openn_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ i2*1
openn_curr_par3_t1 ~ i3*1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strong_openn_curr <- cfa(strong_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_openn_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ i2*1
openn_curr_par3_t1 ~ i3*1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
# Unique Variances
openn_curr_par1_t1 ~~ u1*openn_curr_par1_t1
openn_curr_par2_t1 ~~ u2*openn_curr_par2_t1
openn_curr_par3_t1 ~~ u3*openn_curr_par3_t1
openn_curr_par1_t2 ~~ u1*openn_curr_par1_t2
openn_curr_par2_t2 ~~ u2*openn_curr_par2_t2
openn_curr_par3_t2 ~~ u3*openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strict_openn_curr <- cfa(strict_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_openn_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 223. 7148. 7235. 0.899 0.810 0.194 0.0499
2 weak 715 17 224. 7144. 7222. 0.899 0.849 0.173 0.0504
3 strong 715 15 227. 7143. 7212. 0.899 0.874 0.158 0.0499
4 strict 715 12 233. 7144. 7199. 0.897 0.897 0.143 0.0580# chi-square difference test for nested models
anova(fit_configural_openn_curr, fit_weak_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_openn_curr 8 7147.8 7234.7 223.42
fit_weak_openn_curr 10 7143.9 7221.7 223.55 0.12185 0 2
Pr(>Chisq)
fit_configural_openn_curr
fit_weak_openn_curr 0.9409anova(fit_weak_openn_curr, fit_strong_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_openn_curr 10 7143.9 7221.7 223.55
fit_strong_openn_curr 12 7143.1 7211.7 226.74 3.191 0.028859 2
Pr(>Chisq)
fit_weak_openn_curr
fit_strong_openn_curr 0.2028anova(fit_strong_openn_curr, fit_strict_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_openn_curr 12 7143.1 7211.7 226.74
fit_strict_openn_curr 15 7143.8 7198.7 233.40 6.66 0.041307 3
Pr(>Chisq)
fit_strong_openn_curr
fit_strict_openn_curr 0.08356 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + openn_ideal_par2_t1 + openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + openn_ideal_par2_t2 + openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ 1
openn_ideal_par3_t1 ~ 1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_configural_openn_ideal <- cfa(configural_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_configural_openn_ideal, fit.measures = TRUE)
# Weak invariance model
weak_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ 1
openn_ideal_par3_t1 ~ 1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_weak_openn_ideal <- cfa(weak_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_weak_openn_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ i2*1
openn_ideal_par3_t1 ~ i3*1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strong_openn_ideal <- cfa(strong_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strong_openn_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ i2*1
openn_ideal_par3_t1 ~ i3*1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
# Unique Variances
openn_ideal_par1_t1 ~~ u1*openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ u2*openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ u3*openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ u1*openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ u2*openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ u3*openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strict_openn_ideal <- cfa(strict_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus", missing="ML")
summary(fit_strict_openn_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 715 19 91.6 6737. 6824. 0.938 0.885 0.121 0.0424
2 weak 715 17 91.9 6733. 6811. 0.940 0.909 0.107 0.0432
3 strong 715 15 95.2 6732. 6801. 0.939 0.923 0.0985 0.0464
4 strict 715 12 105. 6736. 6791. 0.934 0.934 0.0916 0.0803# chi-square difference test for nested models
anova(fit_configural_openn_ideal, fit_weak_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_openn_ideal 8 6736.8 6823.7 91.632
fit_weak_openn_ideal 10 6733.1 6810.8 91.936 0.30435 0 2
Pr(>Chisq)
fit_configural_openn_ideal
fit_weak_openn_ideal 0.8588anova(fit_weak_openn_ideal, fit_strong_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_openn_ideal 10 6733.1 6810.8 91.936
fit_strong_openn_ideal 12 6732.4 6801.0 95.230 3.2938 0.030079 2
Pr(>Chisq)
fit_weak_openn_ideal
fit_strong_openn_ideal 0.1926anova(fit_strong_openn_ideal, fit_strict_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_openn_ideal 12 6732.4 6801 95.23
fit_strict_openn_ideal 15 6736.1 6791 104.98 9.745 0.056076 3
Pr(>Chisq)
fit_strong_openn_ideal
fit_strict_openn_ideal 0.02086 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1How many participants at each time point and in each condition?
df_sbsa %>% summarise(n_distinct(pid)) # N (after exclusions)# A tibble: 1 × 1
`n_distinct(pid)`
<int>
1 715df_sbsa %>% group_by(time, rando) %>% tally()# A tibble: 4 × 3
# Groups: time [2]
time rando n
<dbl> <chr> <int>
1 1 Self-Acceptance 343
2 1 Self-Improvement 364
3 2 Self-Acceptance 326
4 2 Self-Improvement 338707 at T1 because for 8 participants we only included valid data from T2.
Gender and age distribution
# across all participants
df_sbsa %>% group_by(pid) %>% slice_head(n=1) %>% ungroup() %>%
mutate(gender_num = ifelse(gender=="Female", 1, 0)) %>%
summarise(n = n(), women = sum(gender_num), m_age = mean(age, na.rm=T), sd_age = sd(age, na.rm=T), min_age = min(age, na.rm=T), max_age = max(age, na.rm=T))# A tibble: 1 × 6
n women m_age sd_age min_age max_age
<int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 715 358 30.7 9.44 19 69# by group and time point
df_sbsa %>% group_by(time, rando) %>%
summarise(n = n(), m_age = mean(age, na.rm=T), sd_age = sd(age, na.rm=T), min_age = min(age, na.rm=T), max_age = max(age, na.rm=T))# A tibble: 4 × 7
# Groups: time [2]
time rando n m_age sd_age min_age max_age
<dbl> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 1 Self-Acceptance 343 30.8 9.18 19 66
2 1 Self-Improvement 364 30.4 9.41 19 69
3 2 Self-Acceptance 326 30.9 9.42 19 66
4 2 Self-Improvement 338 30.6 9.63 19 69df_sbsa %>% group_by(time, rando, gender) %>%
summarise(n = n(), m_age = mean(age, na.rm=T), sd_age = sd(age, na.rm=T), min_age = min(age, na.rm=T), max_age = max(age, na.rm=T))# A tibble: 8 × 8
# Groups: time, rando [4]
time rando gender n m_age sd_age min_age max_age
<dbl> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 1 Self-Acceptance Female 175 30.8 9.10 19 63
2 1 Self-Acceptance Male 168 30.9 9.29 19 66
3 1 Self-Improvement Female 180 30.3 8.89 19 59
4 1 Self-Improvement Male 184 30.5 9.92 19 69
5 2 Self-Acceptance Female 161 30.6 9.34 19 63
6 2 Self-Acceptance Male 165 31.1 9.53 19 66
7 2 Self-Improvement Female 168 30.2 8.82 19 59
8 2 Self-Improvement Male 170 31.0 10.4 19 69Other demographics (from Prolific info):
# across all participants: ethnicity
df_sbsa %>% group_by(pid) %>% slice_head(n=1) %>% ungroup() %>%
group_by(ethnicity) %>% summarise(n = n()) %>% ungroup() %>% mutate(prop = round(n / sum(n) * 100, 3))# A tibble: 6 × 3
ethnicity n prop
<chr> <int> <dbl>
1 Asian 15 2.10
2 Black 193 27.0
3 Mixed 34 4.76
4 Other 12 1.68
5 White 457 63.9
6 <NA> 4 0.559# across all participants: country of residence
df_sbsa %>% group_by(pid) %>% slice_head(n=1) %>% ungroup() %>%
group_by(country) %>% summarise(n = n()) %>% ungroup() %>%
arrange(desc(n)) %>% mutate(prop = round(n / sum(n) * 100, 3)) %>% print(n=20)# A tibble: 30 × 3
country n prop
<chr> <int> <dbl>
1 South Africa 218 30.5
2 Poland 124 17.3
3 United Kingdom 63 8.81
4 Italy 59 8.25
5 Portugal 51 7.13
6 Greece 50 6.99
7 Spain 34 4.76
8 Hungary 27 3.78
9 Czech Republic 13 1.82
10 Mexico 9 1.26
11 Netherlands 9 1.26
12 Estonia 7 0.979
13 Germany 5 0.699
14 Latvia 5 0.699
15 Australia 4 0.559
16 Belgium 4 0.559
17 France 4 0.559
18 Ireland 4 0.559
19 Slovenia 4 0.559
20 Sweden 4 0.559
# ℹ 10 more rows# across all participants: student status
df_sbsa %>% group_by(pid) %>% slice_head(n=1) %>% ungroup() %>%
group_by(student) %>% summarise(n = n()) %>% ungroup() %>%
arrange(desc(n)) %>% mutate(prop = round(n / sum(n) * 100, 3))# A tibble: 3 × 3
student n prop
<chr> <int> <dbl>
1 No 373 52.2
2 Yes 285 39.9
3 <NA> 57 7.97# across all participants: employment status
df_sbsa %>% group_by(pid) %>% slice_head(n=1) %>% ungroup() %>%
group_by(employed) %>% summarise(n = n()) %>% ungroup() %>%
arrange(desc(n)) %>% mutate(prop = round(n / sum(n) * 100, 3))# A tibble: 7 × 3
employed n prop
<chr> <int> <dbl>
1 Full-Time 336 47.0
2 Part-Time 118 16.5
3 Unemployed (and job seeking) 88 12.3
4 <NA> 71 9.93
5 Other 60 8.39
6 Not in paid work (e.g. homemaker', 'retired or disabled) 29 4.06
7 Due to start a new job within the next month 13 1.82On the trait/dimensional level:
# created in study 1 cleaning script
kable(int_consist_traits_st1, digits = 3)| trait | rel_alpha_current | rel_alpha_ideal | rel_omega_t_current | rel_omega_t_ideal | rel_omega_h_current | rel_omega_h_ideal |
|---|---|---|---|---|---|---|
| lifesat | 0.860 | NA | 0.890 | NA | 0.720 | NA |
| meaning | 0.861 | NA | 0.932 | NA | 0.512 | NA |
| selfes | 0.904 | NA | 0.921 | NA | 0.704 | NA |
| concept | 0.879 | NA | 0.900 | NA | 0.748 | NA |
| extra | 0.831 | 0.695 | 0.867 | 0.754 | 0.639 | 0.462 |
| agree | 0.777 | 0.768 | 0.831 | 0.822 | 0.573 | 0.636 |
| consc | 0.876 | 0.793 | 0.899 | 0.827 | 0.726 | 0.682 |
| neuro | 0.894 | 0.762 | 0.913 | 0.808 | 0.733 | 0.674 |
| openn | 0.777 | 0.718 | 0.834 | 0.797 | 0.580 | 0.554 |
McDonald’s omega only really makes sense for the Big Five dimensions (with their three facets each) but I’ve added it for the well-being measures as well. Keep in mind that a few of the omega computations for the well-being measures only converged with error messages. These constructs were not neccessarily intended to be tested in this multidimensional manner for internal consistency.
On the facet level:
# created in study 1 cleaning script
kable(int_consist_facets_st1, digits = 3)| facet | rel_alpha_current | rel_alpha_ideal |
|---|---|---|
| socia | 0.801 | 0.604 |
| asser | 0.697 | 0.531 |
| energ | 0.691 | 0.502 |
| compa | 0.496 | 0.450 |
| respe | 0.694 | 0.623 |
| trust | 0.644 | 0.619 |
| organ | 0.827 | 0.562 |
| produ | 0.770 | 0.640 |
| respo | 0.644 | 0.571 |
| anxie | 0.726 | 0.421 |
| depre | 0.811 | 0.556 |
| volat | 0.810 | 0.651 |
| curio | 0.620 | 0.506 |
| aesth | 0.574 | 0.448 |
| imagi | 0.705 | 0.526 |
Summary stats:
table(df_sbsa$sb06_01) # How much do you want to change your personality in general?
1 2 3 4 5
22 63 153 90 28 summary(df_sbsa$sb06_01) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.00 3.00 3.00 3.11 4.00 5.00 1015 table(df_sbsa$sa06_01) # How much do you want to be better at accepting yourself for who you are?
1 2 3 4 5
6 10 50 110 163 summary(df_sbsa$sa06_01) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 4.000 4.000 4.221 5.000 5.000 1032 Plot:
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb06_01)), aes(x=sb06_01)) + geom_histogram(bins=5) +
labs(x="General change goal", y="Frequency") + theme_bw(),
ggplot(df_sbsa %>% filter(!is.na(sa06_01)), aes(x=sa06_01)) + geom_histogram(bins=5) +
labs(x="General acceptance goal", y="Frequency") + theme_bw(),
ncol = 2
)
Self-Improvement
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you [want to] change. Please rate how much you would like to change your level of each of these traits, where 1 = I do not want to change and 5 = I want to change a lot.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to change in some of these traits. We want to know how you think you changed in each trait. Please rate how much you changed your level of each of these traits during this study, where 1 = I am completely the same and 5 = I have changed a lot.
Self Acceptance
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you accept yourself. Please rate how much you accept your level of each of these traits, where 1 = I completely accept myself and 5 = I want to accept myself more.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to accept your level on some of these traits more. We want to know how much you think you accept yourself more because of this study. Please rate how much you accept your level of each of these traits more during this study, where 1 = I accept myself the same and 5 = I accept this about myself much more.
Plot distributions
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 01], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_01)), aes(x=sb07_01)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_01)), aes(x=sb12_01)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_01)), aes(x=sa07_01)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_01)), aes(x=sa14_01)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 02], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_02)), aes(x=sb07_02)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_02)), aes(x=sb12_02)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_02)), aes(x=sa07_02)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_02)), aes(x=sa14_02)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 03], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_03)), aes(x=sb07_03)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_03)), aes(x=sb12_03)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_03)), aes(x=sa07_03)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_03)), aes(x=sa14_03)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 04], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_04)), aes(x=sb07_04)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_04)), aes(x=sb12_04)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_04)), aes(x=sa07_04)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_04)), aes(x=sa14_04)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 05], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_05)), aes(x=sb07_05)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_05)), aes(x=sb12_05)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_05)), aes(x=sa07_05)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_05)), aes(x=sa14_05)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 06], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_06)), aes(x=sb07_06)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_06)), aes(x=sb12_06)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_06)), aes(x=sa07_06)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_06)), aes(x=sa14_06)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 07], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_07)), aes(x=sb07_07)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_07)), aes(x=sb12_07)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_07)), aes(x=sa07_07)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_07)), aes(x=sa14_07)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 08], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_08)), aes(x=sb07_08)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_08)), aes(x=sb12_08)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_08)), aes(x=sa07_08)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_08)), aes(x=sa14_08)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 09], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_09)), aes(x=sb07_09)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_09)), aes(x=sb12_09)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_09)), aes(x=sa07_09)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_09)), aes(x=sa14_09)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 10], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_10)), aes(x=sb07_10)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_10)), aes(x=sb12_10)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_10)), aes(x=sa07_10)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_10)), aes(x=sa14_10)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 11], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_11)), aes(x=sb07_11)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_11)), aes(x=sb12_11)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_11)), aes(x=sa07_11)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_11)), aes(x=sa14_11)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 12], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_12)), aes(x=sb07_12)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_12)), aes(x=sb12_12)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_12)), aes(x=sa07_12)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_12)), aes(x=sa14_12)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 13], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_13)), aes(x=sb07_13)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_13)), aes(x=sb12_13)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_13)), aes(x=sa07_13)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_13)), aes(x=sa14_13)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 14], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_14)), aes(x=sb07_14)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_14)), aes(x=sb12_14)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_14)), aes(x=sa07_14)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_14)), aes(x=sa14_14)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 15], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_15)), aes(x=sb07_15)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_15)), aes(x=sb12_15)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_15)), aes(x=sa07_15)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_15)), aes(x=sa14_15)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
All four psychological well-being indicators will be positively correlated with a greater similarity between current- and ideal self-ratings of personality.
To examine this at the level of overall profiles, we will compute the correlations between the psychological well-being indicators and the Fisher z transformed correlations between the facet- and item-level real-ideal self-profiles. To examine this at the level of individual traits, we will compute the correlation between psychological well-being indicators and the squared difference between current- and ideal-self rating for each Big Five trait and facet.
Computations:
cormat_profile <- cor(df_sbsa[, c("swls", "meaning", "selfes", "concept",
"profile_corr_item_z", "profile_corr_facet_z")], use = "pairwise.complete.obs")
rownames(cormat_profile) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
"Item-Level Profile Corr (z)", "Facet-Level Profile Corr (z)")
colnames(cormat_profile) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
"Item-Level Profile Corr (z)", "Facet-Level Profile Corr (z)")corrplot(cormat_profile, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.7, diag = FALSE) # also add numbers
Computations:
cormat_sqtraits <- cor(df_sbsa[, c("swls", "meaning", "selfes", "concept",
paste0(str_trunc(names(b5_vars)[1:5], 5, ellipsis = ""), "_sqdiff"))],
use = "pairwise.complete.obs")
rownames(cormat_sqtraits) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[1:5]))
colnames(cormat_sqtraits) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[1:5]))
cormat_sqfacets <- cor(df_sbsa[, c("swls", "meaning", "selfes", "concept",
paste0(str_trunc(names(b5_vars)[6:20], 5, ellipsis = ""), "_sqdiff"))],
use = "pairwise.complete.obs")
rownames(cormat_sqfacets) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[6:20]))
colnames(cormat_sqfacets) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[6:20]))Big Five traits
corrplot(cormat_sqtraits, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.7, diag = FALSE) # also add numbers
Big Five facets
corrplot(cormat_sqfacets, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.6, diag = FALSE) # also add numbers
Additional, exploratory analysis: Are the relationships of the squared trait differences with well-being similar when we control for initial trait levels (at T1)?
Example with extraversion:
cor(df_sbsa %>% filter(rando!="Waitlist-Control" & time==1) %>% pull(swls),
df_sbsa %>% filter(rando!="Waitlist-Control" & time==1) %>% pull(extra_sqdiff),
use = "pairwise.complete.obs") %>% round(2)[1] -0.36# basic model to replicate the correlation
mod_ex_1 <- lm(swls ~ extra_sqdiff, data = df_sbsa %>% filter(rando!="Waitlist-Control" & time==1))
summary(mod_ex_1)$coef Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.9651060 0.04450560 66.62322 5.355442e-304
extra_sqdiff -0.1640454 0.01612553 -10.17303 9.362207e-23effectsize::standardize_parameters(mod_ex_1)# Standardization method: refit
Parameter | Std. Coef. | 95% CI
------------------------------------------
(Intercept) | 2.37e-16 | [-0.07, 0.07]
extra_sqdiff | -0.36 | [-0.43, -0.29]# adjusted model controlling for initial trait level
mod_ex_2 <- lm(swls ~ extra_sqdiff + extra_comb_curr,
data = df_sbsa %>% filter(rando!="Waitlist-Control" & time==1))
summary(mod_ex_2)$coef Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.77356826 0.21622836 8.202293 1.142937e-15
extra_sqdiff -0.07301225 0.02260265 -3.230252 1.295161e-03
extra_comb_curr 0.36661193 0.06516538 5.625870 2.678215e-08effectsize::standardize_parameters(mod_ex_2)# Standardization method: refit
Parameter | Std. Coef. | 95% CI
---------------------------------------------
(Intercept) | 1.78e-16 | [-0.07, 0.07]
extra_sqdiff | -0.16 | [-0.26, -0.06]
extra_comb_curr | 0.28 | [ 0.18, 0.38]Only T1:
# prepare df
df_check_adjusted <- df_sbsa %>%
# reshape with well-being outcomes and current trait levels
filter(rando!="Waitlist-Control" & time==1) %>%
select(pid, rando,
swls, meaning, selfes, concept,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_curr")) %>%
pivot_longer(ends_with("_comb_curr"),
names_to = "test", names_prefix = "facet", values_to = "initial", values_drop_na = TRUE) %>%
mutate(test = sub("_.*", "", test)) %>%
left_join(
df_sbsa %>%
# reshape with squared differences -> add to previous
filter(rando!="Waitlist-Control" & time==1) %>%
select(pid, rando, ends_with("_sqdiff")) %>%
pivot_longer(ends_with("_sqdiff"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
mutate(test = sub("_.*", "", test))
)
# run models
# swls
df_check_adjusted_swls <- df_check_adjusted %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lm(score ~ swls + initial, data = .x))) %>%
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_swls_unlist <- as.data.frame(summary(df_check_adjusted_swls[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_swls[[1]])$Std_Coefficient) # standardized beta
for (i in 2:length(df_check_adjusted_swls)) {
df_check_adjusted_swls_unlist <-
bind_rows(df_check_adjusted_swls_unlist,
as.data.frame(summary(df_check_adjusted_swls[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_swls[[i]])$Std_Coefficient))
}
df_check_adjusted_swls_unlist <- df_check_adjusted_swls_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "swls", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))
# meaning
df_check_adjusted_meaning <- df_check_adjusted %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lm(score ~ meaning + initial, data = .x))) %>%
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_meaning_unlist <- as.data.frame(summary(df_check_adjusted_meaning[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_meaning[[1]])$Std_Coefficient) # standardized beta
for (i in 2:length(df_check_adjusted_meaning)) {
df_check_adjusted_meaning_unlist <-
bind_rows(df_check_adjusted_meaning_unlist,
as.data.frame(summary(df_check_adjusted_meaning[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_meaning[[i]])$Std_Coefficient))
}
df_check_adjusted_meaning_unlist <- df_check_adjusted_meaning_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "meaning", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))
# selfes
df_check_adjusted_selfes <- df_check_adjusted %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lm(score ~ selfes + initial, data = .x))) %>%
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_selfes_unlist <- as.data.frame(summary(df_check_adjusted_selfes[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_selfes[[1]])$Std_Coefficient) # standardized beta
for (i in 2:length(df_check_adjusted_selfes)) {
df_check_adjusted_selfes_unlist <-
bind_rows(df_check_adjusted_selfes_unlist,
as.data.frame(summary(df_check_adjusted_selfes[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_selfes[[i]])$Std_Coefficient))
}
df_check_adjusted_selfes_unlist <- df_check_adjusted_selfes_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "selfes", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))
# concept
df_check_adjusted_concept <- df_check_adjusted %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lm(score ~ concept + initial, data = .x))) %>%
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_concept_unlist <- as.data.frame(summary(df_check_adjusted_concept[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_concept[[1]])$Std_Coefficient) # standardized beta
for (i in 2:length(df_check_adjusted_concept)) {
df_check_adjusted_concept_unlist <-
bind_rows(df_check_adjusted_concept_unlist,
as.data.frame(summary(df_check_adjusted_concept[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_concept[[i]])$Std_Coefficient))
}
df_check_adjusted_concept_unlist <- df_check_adjusted_concept_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "concept", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))Does the relationship between the squared difference in one trait (current vs. ideal) and well-being aspects still hold if we control for the initial trait level?
Combine as one table:
kable(df_check_adjusted_swls_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_swls = d, p_ls = p, sig_ls = sig) %>%
left_join(
df_check_adjusted_meaning_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_meaning = d, p_m = p, sig_m = sig)
) %>%
left_join(
df_check_adjusted_selfes_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_selfes = d, p_se = p, sig_se = sig)
) %>%
left_join(
df_check_adjusted_concept_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_concept = d, p_c = p, sig_c = sig)
),
digits = 3)| outcome | d_swls | p_ls | sig_ls | d_meaning | p_m | sig_m | d_selfes | p_se | sig_se | d_concept | p_c | sig_c |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| extraversion | -0.092 | 0.001 | ** | -0.066 | 0.017 | * | -0.062 | 0.050 | -0.032 | 0.254 | ||
| agreeableness | -0.090 | 0.007 | ** | -0.082 | 0.014 | * | -0.078 | 0.020 | * | -0.087 | 0.010 | ** |
| conscientiousness | -0.104 | 0.000 | *** | -0.117 | 0.000 | *** | -0.063 | 0.024 | * | -0.049 | 0.063 | |
| neuroticism | -0.083 | 0.001 | *** | -0.079 | 0.001 | ** | -0.029 | 0.317 | 0.029 | 0.259 | ||
| openness | -0.099 | 0.002 | ** | -0.128 | 0.000 | *** | -0.111 | 0.001 | *** | -0.059 | 0.071 | |
| sociability | -0.137 | 0.000 | *** | -0.092 | 0.002 | ** | -0.081 | 0.008 | ** | -0.051 | 0.076 | |
| assertiveness | -0.082 | 0.007 | ** | -0.073 | 0.016 | * | -0.030 | 0.361 | -0.033 | 0.280 | ||
| energy | -0.110 | 0.000 | *** | -0.055 | 0.054 | -0.065 | 0.050 | * | -0.040 | 0.165 | ||
| compassion | -0.063 | 0.085 | -0.066 | 0.072 | -0.104 | 0.004 | ** | -0.084 | 0.022 | * | ||
| respectfulness | -0.077 | 0.013 | * | -0.043 | 0.167 | -0.025 | 0.428 | -0.074 | 0.019 | * | ||
| trust | -0.090 | 0.006 | ** | -0.096 | 0.003 | ** | -0.095 | 0.005 | ** | -0.100 | 0.003 | ** |
| organization | -0.086 | 0.001 | *** | -0.108 | 0.000 | *** | -0.035 | 0.185 | -0.033 | 0.197 | ||
| productiveness | -0.072 | 0.002 | ** | -0.069 | 0.003 | ** | -0.044 | 0.086 | -0.031 | 0.196 | ||
| responsibility | -0.143 | 0.000 | *** | -0.107 | 0.000 | *** | -0.074 | 0.021 | * | -0.049 | 0.123 | |
| anxiety | -0.063 | 0.017 | * | -0.064 | 0.014 | * | -0.028 | 0.338 | 0.015 | 0.585 | ||
| depression | -0.100 | 0.000 | *** | -0.081 | 0.001 | *** | -0.047 | 0.117 | 0.015 | 0.523 | ||
| volatility | -0.090 | 0.000 | *** | -0.083 | 0.001 | *** | -0.064 | 0.016 | * | -0.006 | 0.827 | |
| curiosity | -0.091 | 0.007 | ** | -0.110 | 0.001 | ** | -0.083 | 0.014 | * | -0.069 | 0.039 | * |
| aesthetic | -0.059 | 0.100 | -0.065 | 0.072 | -0.025 | 0.483 | -0.022 | 0.541 | ||||
| imagination | -0.074 | 0.007 | ** | -0.097 | 0.000 | *** | -0.077 | 0.007 | ** | -0.021 | 0.456 |
Across all time points, using a mixed model:
# prepare df
df_check_adjusted_all <- df_sbsa %>%
# reshape with well-being outcomes and current trait levels
filter(rando!="Waitlist-Control") %>%
select(pid, rando, time,
swls, meaning, selfes, concept,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff")) %>%
pivot_longer(ends_with("_sqdiff"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
mutate(test = sub("_.*", "", test)) %>%
left_join(
df_sbsa %>%
# reshape with squared differences -> add to previous
filter(rando!="Waitlist-Control" & time==1) %>%
select(pid, rando, ends_with("_comb_curr")) %>%
pivot_longer(ends_with("_comb_curr"),
names_to = "test", names_prefix = "facet", values_to = "initial", values_drop_na = TRUE) %>%
mutate(test = sub("_.*", "", test))
)
# run models
# swls
df_check_adjusted_swls_all <- df_check_adjusted_all %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lmerTest::lmer(score ~ swls + initial + (1 | pid), data = .x))) %>% # random intercept model
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_swls_all_unlist <- as.data.frame(summary(df_check_adjusted_swls_all[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_swls_all[[1]])$Std_Coefficient) # standardized beta
for (i in 2:length(df_check_adjusted_swls_all)) {
df_check_adjusted_swls_all_unlist <-
bind_rows(df_check_adjusted_swls_all_unlist,
as.data.frame(summary(df_check_adjusted_swls_all[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_swls_all[[i]])$Std_Coefficient))
}
df_check_adjusted_swls_all_unlist <- df_check_adjusted_swls_all_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "swls", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))
# meaning
df_check_adjusted_meaning_all <- df_check_adjusted_all %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lmerTest::lmer(score ~ meaning + initial + (1 | pid), data = .x))) %>% # random intercept model
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_meaning_all_unlist <- as.data.frame(summary(df_check_adjusted_meaning_all[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_meaning_all[[1]])$Std_Coefficient)
for (i in 2:length(df_check_adjusted_meaning_all)) {
df_check_adjusted_meaning_all_unlist <-
bind_rows(df_check_adjusted_meaning_all_unlist,
as.data.frame(summary(df_check_adjusted_meaning_all[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_meaning_all[[i]])$Std_Coefficient))
}
df_check_adjusted_meaning_all_unlist <- df_check_adjusted_meaning_all_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "meaning", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))
# selfes
df_check_adjusted_selfes_all <- df_check_adjusted_all %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lmerTest::lmer(score ~ selfes + initial + (1 | pid), data = .x))) %>% # random intercept model
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_selfes_all_unlist <- as.data.frame(summary(df_check_adjusted_selfes_all[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_selfes_all[[1]])$Std_Coefficient)
for (i in 2:length(df_check_adjusted_selfes_all)) {
df_check_adjusted_selfes_all_unlist <-
bind_rows(df_check_adjusted_selfes_all_unlist,
as.data.frame(summary(df_check_adjusted_selfes_all[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_selfes_all[[i]])$Std_Coefficient))
}
df_check_adjusted_selfes_all_unlist <- df_check_adjusted_selfes_all_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "selfes", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))
# concept
df_check_adjusted_concept_all <- df_check_adjusted_all %>%
group_nest(test) %>%
mutate(lm_mods = map(data, ~lmerTest::lmer(score ~ concept + initial + (1 | pid), data = .x))) %>% # random intercept model
pull(lm_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_check_adjusted_concept_all_unlist <- as.data.frame(summary(df_check_adjusted_concept_all[[1]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_concept_all[[1]])$Std_Coefficient)
for (i in 2:length(df_check_adjusted_concept_all)) {
df_check_adjusted_concept_all_unlist <-
bind_rows(df_check_adjusted_concept_all_unlist,
as.data.frame(summary(df_check_adjusted_concept_all[[i]])$coefficients) %>% as_tibble() %>%
mutate(d = effectsize::standardize_parameters(df_check_adjusted_concept_all[[i]])$Std_Coefficient))
}
df_check_adjusted_concept_all_unlist <- df_check_adjusted_concept_all_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=3),
term = c(rep(c("Intercept", "concept", "initial"), 20))) %>%
rename(p = `Pr(>|t|)`, std_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, d, std_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars))))Does the relationship between the squared difference in one trait (current vs. ideal) and well-being aspects still hold if we control for the initial trait level?
Combine as one table:
kable(df_check_adjusted_swls_all_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_swls = d, p_ls = p, sig_ls = sig) %>%
left_join(
df_check_adjusted_meaning_all_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_meaning = d, p_m = p, sig_m = sig)
) %>%
left_join(
df_check_adjusted_selfes_all_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_selfes = d, p_se = p, sig_se = sig)
) %>%
left_join(
df_check_adjusted_concept_all_unlist %>%
filter(!term %in% c("Intercept", "initial")) %>%
select(outcome, d_concept = d, p_c = p, sig_c = sig)
),
digits = 3)| outcome | d_swls | p_ls | sig_ls | d_meaning | p_m | sig_m | d_selfes | p_se | sig_se | d_concept | p_c | sig_c |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| extraversion | -0.215 | 0.000 | *** | -0.177 | 0.000 | *** | -0.246 | 0.000 | *** | -0.144 | 0.000 | *** |
| agreeableness | -0.135 | 0.000 | *** | -0.122 | 0.000 | *** | -0.166 | 0.000 | *** | -0.153 | 0.000 | *** |
| conscientiousness | -0.159 | 0.000 | *** | -0.142 | 0.000 | *** | -0.176 | 0.000 | *** | -0.127 | 0.000 | *** |
| neuroticism | -0.216 | 0.000 | *** | -0.183 | 0.000 | *** | -0.277 | 0.000 | *** | -0.139 | 0.000 | *** |
| openness | -0.122 | 0.000 | *** | -0.143 | 0.000 | *** | -0.151 | 0.000 | *** | -0.084 | 0.001 | *** |
| sociability | -0.235 | 0.000 | *** | -0.170 | 0.000 | *** | -0.216 | 0.000 | *** | -0.123 | 0.000 | *** |
| assertiveness | -0.148 | 0.000 | *** | -0.155 | 0.000 | *** | -0.165 | 0.000 | *** | -0.130 | 0.000 | *** |
| energy | -0.216 | 0.000 | *** | -0.155 | 0.000 | *** | -0.223 | 0.000 | *** | -0.137 | 0.000 | *** |
| compassion | -0.109 | 0.000 | *** | -0.091 | 0.001 | *** | -0.152 | 0.000 | *** | -0.135 | 0.000 | *** |
| respectfulness | -0.100 | 0.000 | *** | -0.094 | 0.000 | *** | -0.102 | 0.000 | *** | -0.138 | 0.000 | *** |
| trust | -0.130 | 0.000 | *** | -0.121 | 0.000 | *** | -0.167 | 0.000 | *** | -0.142 | 0.000 | *** |
| organization | -0.098 | 0.000 | *** | -0.110 | 0.000 | *** | -0.102 | 0.000 | *** | -0.102 | 0.000 | *** |
| productiveness | -0.159 | 0.000 | *** | -0.115 | 0.000 | *** | -0.172 | 0.000 | *** | -0.101 | 0.000 | *** |
| responsibility | -0.171 | 0.000 | *** | -0.116 | 0.000 | *** | -0.171 | 0.000 | *** | -0.116 | 0.000 | *** |
| anxiety | -0.175 | 0.000 | *** | -0.148 | 0.000 | *** | -0.198 | 0.000 | *** | -0.093 | 0.000 | *** |
| depression | -0.246 | 0.000 | *** | -0.202 | 0.000 | *** | -0.348 | 0.000 | *** | -0.143 | 0.000 | *** |
| volatility | -0.168 | 0.000 | *** | -0.150 | 0.000 | *** | -0.192 | 0.000 | *** | -0.139 | 0.000 | *** |
| curiosity | -0.089 | 0.001 | *** | -0.094 | 0.000 | *** | -0.092 | 0.000 | *** | -0.077 | 0.003 | ** |
| aesthetic | -0.064 | 0.021 | * | -0.067 | 0.013 | * | -0.046 | 0.094 | -0.050 | 0.068 | ||
| imagination | -0.101 | 0.000 | *** | -0.109 | 0.000 | *** | -0.128 | 0.000 | *** | -0.056 | 0.015 | * |
Both groups will increase in all four psychological well-being indicators.
We will test the mean-level difference between baseline and follow up using a latent change model.
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_swls_hyp2 <- '
swls_t1 =~ 1*sw06_01_t1 + lamb2*sw06_02_t1 + lamb3*sw06_03_t1 + lamb4*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + lamb2*sw06_02_t2 + lamb3*sw06_03_t2 + lamb4*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ 1 # This estimates the intercept of the change score
swls_t1 ~ 1 # This estimates the intercept of swls_t1
d_swls_1 ~~ d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~~ swls_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
sw06_01_t1 ~~ sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ res1*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ res2*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ res3*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ res4*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ res1*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ res2*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ res3*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ res4*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ m4*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ m4*1 # This estimates the intercept of X4 at T2
'
fit_mi_lcs_swls_hyp2 <- lavaan(mi_lcs_swls_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_swls_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_swls_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 714 | 19 | 14257.04 | 14343.89 | 0.991 | 0.04 | 0.029 |
# parameters of interest
params_lcs_swls_hyp2 <- broom::tidy(fit_mi_lcs_swls_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("swls_t2 ~ swls_t1", "d_swls_1 =~ swls_t2", "swls_t1 ~~ d_swls_1", # change parameters
"d_swls_1 ~1 ", "swls_t1 ~1 ", "", # means
"d_swls_1 ~~ d_swls_1")) # variances
kable(params_lcs_swls_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| swls_t2 ~ swls_t1 | 1.000 | 1.000 | 1.000 | 0.936 | NA | NA |
| d_swls_1 =~ swls_t2 | 1.000 | 1.000 | 1.000 | 0.662 | NA | NA |
| d_swls_1 ~1 | 0.288 | 0.226 | 0.351 | 0.435 | 9.048 | 0 |
| swls_t1 ~1 | 2.696 | 2.614 | 2.778 | 2.872 | 64.385 | 0 |
| d_swls_1 ~~ d_swls_1 | 0.441 | 0.351 | 0.530 | 1.000 | 9.644 | 0 |
| swls_t1 ~~ d_swls_1 | -0.158 | -0.217 | -0.099 | -0.253 | -5.237 | 0 |
Plot model:
semPaths(fit_mi_lcs_swls_hyp2, what="est",
sizeLat = 7, sizeMan = 7, edge.label.cex = .75, intercepts = T, edge.color="black")
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_meaning_hyp2 <- '
meaning_t1 =~ 1*meaning_par1_t1 + lamb2*meaning_par2_t1 + lamb3*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + lamb2*meaning_par2_t2 + lamb3*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ 1 # This estimates the intercept of the change score
meaning_t1 ~ 1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~~ meaning_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
meaning_par1_t1 ~~ meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ res1*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ res2*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ res3*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ res1*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ res2*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ res3*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_meaning_hyp2 <- lavaan(mi_lcs_meaning_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_meaning_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_meaning_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 714 | 15 | 10454.07 | 10522.63 | 0.995 | 0.042 | 0.025 |
# parameters of interest
params_lcs_meaning_hyp2 <- broom::tidy(fit_mi_lcs_meaning_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("meaning_t2 ~ meaning_t1", "d_meaning_1 =~ meaning_t2", "meaning_t1 ~~ d_meaning_1", # change parameters
"d_meaning_1 ~1 ", "meaning_t1 ~1 ", "", # means
"d_meaning_1 ~~ d_meaning_1")) # variances
kable(params_lcs_meaning_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| meaning_t2 ~ meaning_t1 | 1.000 | 1.000 | 1.000 | 0.957 | NA | NA |
| d_meaning_1 =~ meaning_t2 | 1.000 | 1.000 | 1.000 | 0.843 | NA | NA |
| d_meaning_1 ~1 | 0.279 | 0.208 | 0.349 | 0.341 | 7.721 | 0 |
| meaning_t1 ~1 | 3.216 | 3.133 | 3.299 | 3.467 | 76.110 | 0 |
| d_meaning_1 ~~ d_meaning_1 | 0.668 | 0.524 | 0.813 | 1.000 | 9.046 | 0 |
| meaning_t1 ~~ d_meaning_1 | -0.295 | -0.377 | -0.213 | -0.389 | -7.027 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_selfes_hyp2 <- '
selfes_t1 =~ 1*selfes_par1_t1 + lamb2*selfes_par2_t1 + lamb3*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + lamb2*selfes_par2_t2 + lamb3*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ 1 # This estimates the intercept of the change score
selfes_t1 ~ 1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~~ selfes_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
selfes_par1_t1 ~~ selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ res1*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ res2*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ res3*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ res1*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ res2*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ res3*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_selfes_hyp2 <- lavaan(mi_lcs_selfes_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_selfes_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_selfes_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 714 | 15 | 7336.702 | 7405.265 | 1 | 0 | 0.009 |
# parameters of interest
params_lcs_selfes_hyp2 <- broom::tidy(fit_mi_lcs_selfes_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("selfes_t2 ~ selfes_t1", "d_selfes_1 =~ selfes_t2", "selfes_t1 ~~ d_selfes_1", # change parameters
"d_selfes_1 ~1 ", "selfes_t1 ~1 ", "", # means
"d_selfes_1 ~~ d_selfes_1")) # variances
kable(params_lcs_selfes_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| selfes_t2 ~ selfes_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_selfes_1 =~ selfes_t2 | 1.000 | 1.000 | 1.000 | 0.720 | NA | NA |
| d_selfes_1 ~1 | 0.285 | 0.238 | 0.332 | 0.520 | 11.953 | 0 |
| selfes_t1 ~1 | 3.127 | 3.065 | 3.190 | 4.138 | 97.548 | 0 |
| d_selfes_1 ~~ d_selfes_1 | 0.300 | 0.244 | 0.357 | 1.000 | 10.402 | 0 |
| selfes_t1 ~~ d_selfes_1 | -0.146 | -0.186 | -0.106 | -0.352 | -7.150 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_concept_hyp2 <- '
concept_t1 =~ 1*concept_par1_t1 + lamb2*concept_par2_t1 + lamb3*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + lamb2*concept_par2_t2 + lamb3*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ 1 # This estimates the intercept of the change score
concept_t1 ~ 1 # This estimates the intercept of concept_t1
d_concept_1 ~~ d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~~ concept_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
concept_par1_t1 ~~ concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ res1*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ res2*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ res3*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ res1*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ res2*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ res3*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_concept_hyp2 <- lavaan(mi_lcs_concept_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_concept_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_concept_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 714 | 15 | 8360.688 | 8429.252 | 0.998 | 0.023 | 0.022 |
# parameters of interest
params_lcs_concept_hyp2 <- broom::tidy(fit_mi_lcs_concept_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("concept_t2 ~ concept_t1", "d_concept_1 =~ concept_t2", "concept_t1 ~~ d_concept_1", # change parameters
"d_concept_1 ~1 ", "concept_t1 ~1 ", "", # means
"d_concept_1 ~~ d_concept_1")) # variances
kable(params_lcs_concept_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| concept_t2 ~ concept_t1 | 1.000 | 1.000 | 1.000 | 0.960 | NA | NA |
| d_concept_1 =~ concept_t2 | 1.000 | 1.000 | 1.000 | 0.825 | NA | NA |
| d_concept_1 ~1 | 0.202 | 0.154 | 0.250 | 0.360 | 8.188 | 0 |
| concept_t1 ~1 | 2.928 | 2.871 | 2.986 | 4.483 | 99.444 | 0 |
| d_concept_1 ~~ d_concept_1 | 0.315 | 0.253 | 0.377 | 1.000 | 9.927 | 0 |
| concept_t1 ~~ d_concept_1 | -0.139 | -0.176 | -0.103 | -0.380 | -7.512 | 0 |
The distance between ideal- and current-self will decrease in both groups.
We will use two strategies to test this hypothesis. First, we will compute the Fisher z-transformed profile correlation between current- and ideal-self and test whether it increased across assessments. Second, we will test whether the squared difference between current- and ideal-self ratings for each Big Five trait decreased across assessments. We will test mean-level differences in profile correlations and squared differences between baseline and follow up using repeated-measures t-test.
Reshape to wide:
# reshape to wide
df_sbsa_wide_profdiff <- df_sbsa %>%
arrange(pid, time) %>%
select(pid, time, profile_corr_item_z, profile_corr_facet_z, ends_with("_sqdiff")) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = -c(pid, time))Checking assumptions
# item-level profile correlation
shapiro.test(df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1
W = 0.92573, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1
D = 0.088592, p-value = 2.99e-13# descriptives
psych::describe(df_sbsa_wide_profdiff$profile_corr_item_z_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 0.37 0.47 0.36 0.35 0.45 -1.05 2.25 3.3 0.49 0.88 0.02psych::describe(df_sbsa_wide_profdiff$profile_corr_item_z_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 0.55 0.53 0.51 0.53 0.5 -0.9 3.32 4.22 0.71 2.48 0.02# facet-level profile correlation
shapiro.test(df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1
W = 0.94494, p-value = 7.556e-15lillie.test(df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1
D = 0.076821, p-value = 8.307e-10# descriptives
psych::describe(df_sbsa_wide_profdiff$profile_corr_facet_z_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 0.28 0.65 0.22 0.25 0.64 -1.5 2.67 4.17 0.5 0.51 0.02psych::describe(df_sbsa_wide_profdiff$profile_corr_facet_z_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 0.5 0.73 0.47 0.48 0.73 -1.22 3.73 4.96 0.46 0.78 0.03Results summary:
t_profile_corr_item <- t.test(df_sbsa_wide_profdiff$profile_corr_item_z_t2, df_sbsa_wide_profdiff$profile_corr_item_z_t1, paired = TRUE)
t_profile_corr_facet <- t.test(df_sbsa_wide_profdiff$profile_corr_facet_z_t2, df_sbsa_wide_profdiff$profile_corr_facet_z_t1, paired = TRUE)
kable(bind_rows(tidy(t_profile_corr_item), tidy(t_profile_corr_facet)) %>% mutate(outcome = c("Item-level profile corr.", "Facet-level profile corr.")) %>% select(outcome, everything()), digits = 3)| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| Item-level profile corr. | 0.181 | 11.457 | 0 | 652 | 0.150 | 0.212 | Paired t-test | two.sided |
| Facet-level profile corr. | 0.227 | 10.775 | 0 | 652 | 0.186 | 0.268 | Paired t-test | two.sided |
# effect sizes
d_profile_corr_item <- psych::cohen.d(profile_corr_item_z ~ time, data = df_sbsa)
d_profile_corr_item$cohen.d lower effect upper
profile_corr_item_z 0.2563996 0.3633649 0.4701992d_profile_corr_facet <- psych::cohen.d(profile_corr_facet_z ~ time, data = df_sbsa)
d_profile_corr_facet$cohen.d lower effect upper
profile_corr_facet_z 0.221792 0.3285939 0.435276Plot
profile_df_plot <- df_sbsa %>% select(pid, time, profile_corr_item_z, profile_corr_facet_z) %>%
pivot_longer(-c(pid, time), names_to = "itemfacet", values_to = "corr") %>%
mutate(itemfacet2 = fct_recode(itemfacet, "Item-level" = "profile_corr_item_z", "Facet-level" = "profile_corr_facet_z"),
itemfacet2 = fct_reorder(itemfacet2, corr, .desc = F))
ggplot(profile_df_plot) +
aes(x = as.factor(time), y = corr) +
geom_boxplot() +
geom_violin(fill = NA) +
facet_wrap(vars(itemfacet2)) +
labs(x = "Measurement Occasion", y = "Profile correlation",
title = "H3: Distance between ideal- and current-self") +
theme_bw()
Checking assumptions
# extra
shapiro.test(df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1
W = 0.869, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1
D = 0.15365, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$extra_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 1.85 2.05 1.17 1.47 1.48 0 11.11 11.11 1.76 3.26 0.08psych::describe(df_sbsa_wide_profdiff$extra_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 1.34 1.62 0.77 1.01 0.88 0 10.56 10.56 2.03 4.65 0.06# agree
shapiro.test(df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1
W = 0.77856, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1
D = 0.19487, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$agree_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 0.55 0.91 0.25 0.35 0.33 0 8.03 8.03 3.75 19.28 0.03psych::describe(df_sbsa_wide_profdiff$agree_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 0.4 0.74 0.14 0.24 0.2 0 10.03 10.03 5.7 54.3 0.03# consc
shapiro.test(df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1
W = 0.83389, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1
D = 0.15295, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$consc_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 1.71 2.08 1 1.32 1.32 0 14.06 14.06 2.23 6.73 0.08psych::describe(df_sbsa_wide_profdiff$consc_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 1.37 1.78 0.69 1.02 0.99 0 14.69 14.69 2.68 10.82 0.07# neuro
shapiro.test(df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1
W = 0.91045, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1
D = 0.11858, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$neuro_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 3.29 3.41 2.25 2.72 2.68 0 16 16 1.4 1.5 0.13psych::describe(df_sbsa_wide_profdiff$neuro_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 2.67 3.09 1.56 2.07 1.95 0 15.34 15.34 1.74 2.78 0.12# openn
shapiro.test(df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1
W = 0.80041, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1
D = 0.19929, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$openn_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 704 0.48 0.8 0.17 0.29 0.25 0 5.84 5.84 2.96 10.71 0.03psych::describe(df_sbsa_wide_profdiff$openn_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 664 0.37 0.61 0.11 0.23 0.15 0 4.34 4.34 3.05 11.98 0.02Results summary:
mod_traits_sqdiff <- df_sbsa_wide_profdiff %>%
select(-starts_with("profile")) %>%
pivot_longer(-c(pid),
names_to = c("test", "time"), values_to = "score",
names_pattern = "(.*)_(t1|t2)") %>%
filter(!is.na(score)) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(t_tests = map(data, ~t.test(score ~ relevel(as.factor(time), "t2"), # need to relevel this to get T2-T1 diff (not sure why)
data = .x, paired = TRUE))) %>%
pull(t_tests) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
mod_traits_unlist <- mod_traits_sqdiff[[1]] %>% tidy()
for (i in 2:length(mod_traits_sqdiff)) {
mod_traits_unlist <- bind_rows(mod_traits_unlist, mod_traits_sqdiff[[i]] %>% tidy())
}
kable(mod_traits_unlist %>% mutate(outcome = sort(names(b5_vars))) %>%
select(outcome, everything()) %>% arrange(factor(outcome, levels = (names(b5_vars)))), digits = 3) # order by BFI traits| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| extraversion | -0.504 | -7.743 | 0.000 | 652 | -0.632 | -0.376 | Paired t-test | two.sided |
| agreeableness | -0.161 | -5.348 | 0.000 | 652 | -0.220 | -0.102 | Paired t-test | two.sided |
| conscientiousness | -0.323 | -5.037 | 0.000 | 652 | -0.448 | -0.197 | Paired t-test | two.sided |
| neuroticism | -0.672 | -7.043 | 0.000 | 652 | -0.860 | -0.485 | Paired t-test | two.sided |
| openness | -0.118 | -4.269 | 0.000 | 652 | -0.173 | -0.064 | Paired t-test | two.sided |
| sociability | -0.814 | -8.158 | 0.000 | 652 | -1.010 | -0.618 | Paired t-test | two.sided |
| assertiveness | -0.491 | -5.652 | 0.000 | 652 | -0.661 | -0.320 | Paired t-test | two.sided |
| energy | -0.490 | -5.238 | 0.000 | 652 | -0.674 | -0.307 | Paired t-test | two.sided |
| compassion | -0.150 | -3.933 | 0.000 | 652 | -0.225 | -0.075 | Paired t-test | two.sided |
| respectfulness | -0.165 | -3.930 | 0.000 | 652 | -0.247 | -0.082 | Paired t-test | two.sided |
| trust | -0.354 | -5.091 | 0.000 | 652 | -0.490 | -0.217 | Paired t-test | two.sided |
| organization | -0.374 | -4.056 | 0.000 | 652 | -0.556 | -0.193 | Paired t-test | two.sided |
| productiveness | -0.529 | -5.388 | 0.000 | 652 | -0.722 | -0.336 | Paired t-test | two.sided |
| responsibility | -0.245 | -3.659 | 0.000 | 652 | -0.377 | -0.114 | Paired t-test | two.sided |
| anxiety | -0.699 | -4.979 | 0.000 | 652 | -0.975 | -0.423 | Paired t-test | two.sided |
| depression | -0.901 | -7.331 | 0.000 | 652 | -1.143 | -0.660 | Paired t-test | two.sided |
| volatility | -0.617 | -5.618 | 0.000 | 652 | -0.833 | -0.402 | Paired t-test | two.sided |
| curiosity | -0.156 | -3.434 | 0.001 | 652 | -0.245 | -0.067 | Paired t-test | two.sided |
| aesthetic | -0.129 | -2.877 | 0.004 | 652 | -0.216 | -0.041 | Paired t-test | two.sided |
| imagination | -0.238 | -3.573 | 0.000 | 652 | -0.369 | -0.107 | Paired t-test | two.sided |
# effect sizes
cohend_cust = function(outcome, df) {
form = as.formula(paste0(outcome, " ~ time"))
psych::cohen.d(form, data = df)
}
d_trait <- cohend_cust(paste0(str_trunc(names(b5_vars)[1], 5, ellipsis = ""), "_sqdiff"), df_sbsa)
d_traits_unlist <- as_tibble(d_trait$cohen.d)
for (i in 2:length(names(b5_vars))) {
d_trait <- cohend_cust(paste0(str_trunc(names(b5_vars)[i], 5, ellipsis = ""), "_sqdiff"), df_sbsa)
d_traits_unlist <- bind_rows(d_traits_unlist, as_tibble(d_trait$cohen.d))
}
kable(d_traits_unlist %>% mutate(outcome = names(b5_vars)) %>% select(outcome, everything()), digits = 3)| outcome | lower | effect | upper |
|---|---|---|---|
| extraversion | -0.380 | -0.274 | -0.167 |
| agreeableness | -0.289 | -0.183 | -0.077 |
| conscientiousness | -0.282 | -0.176 | -0.069 |
| neuroticism | -0.298 | -0.192 | -0.085 |
| openness | -0.269 | -0.163 | -0.056 |
| sociability | -0.375 | -0.268 | -0.162 |
| assertiveness | -0.320 | -0.214 | -0.108 |
| energy | -0.324 | -0.217 | -0.111 |
| compassion | -0.272 | -0.166 | -0.060 |
| respectfulness | -0.251 | -0.145 | -0.039 |
| trust | -0.301 | -0.195 | -0.089 |
| organization | -0.267 | -0.160 | -0.054 |
| productiveness | -0.290 | -0.184 | -0.077 |
| responsibility | -0.244 | -0.138 | -0.031 |
| anxiety | -0.258 | -0.151 | -0.045 |
| depression | -0.339 | -0.233 | -0.127 |
| volatility | -0.275 | -0.169 | -0.063 |
| curiosity | -0.251 | -0.145 | -0.039 |
| aesthetic | -0.258 | -0.152 | -0.046 |
| imagination | -0.224 | -0.118 | -0.012 |
Plot (traits/facets sorted by average size of squared differences)
sqdiff_df_plot <- df_sbsa %>%
select(pid, time, all_of(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"))) %>%
pivot_longer(-c(pid, time), names_to = "trait", values_to = "sqdiff") %>%
group_by(trait) %>%
mutate(cur_group_id()) %>%
ungroup() %>%
mutate(trait2 = fct_recode(trait, !!! setNames(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"),
str_to_title(names(b5_vars)))),
trait2 = fct_reorder(trait2, sqdiff, .desc = T))
plot_sqdiff <- ggplot(sqdiff_df_plot) +
aes(x = as.factor(time), y = sqdiff) +
geom_boxplot() +
geom_violin(fill = NA) +
ggforce::facet_wrap_paginate(~trait2, ncol=3, nrow=2) +
labs(x = "Measurement Occasion", y = "Squared difference") +
theme_bw()
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 1)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 2)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 3)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 4)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 5)
In the self-improvement group, there will be a correlation between change goals and change in current-self ratings but not change in ideal-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level differences across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sb <- df_sbsa %>%
filter(rando=="Self-Improvement") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07")), # facet-specific change goals
sb06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sb07"), sb06_01)) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2, sb06_01_t2))
# colnames(df_sbsa_wide_pers_sb)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sb <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Improvement") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sb)Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_hyp4 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~~ extra_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_extra_curr_hyp4 <- lavaan(mi_lcs_extra_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4746.072 | 4816.418 | 0.983 | 0.058 | 0.081 |
# parameters of interest
params_lcs_extra_curr_hyp4 <- broom::tidy(fit_mi_lcs_extra_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.590 | NA | NA |
| d_extra_1 ~1 | 0.134 | 0.085 | 0.182 | 0.388 | 5.431 | 0.000 |
| extra_t1 ~1 | 2.756 | 2.684 | 2.827 | 4.790 | 75.483 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.119 | 0.073 | 0.165 | 1.000 | 5.033 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.054 | -0.086 | -0.023 | -0.274 | -3.366 | 0.001 |
| d_extra_1 ~~ sb06_01_t1 | 0.064 | 0.012 | 0.116 | 0.187 | 2.423 | 0.015 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.193 | 0.000 |
| sb06_01_t1 ~1 | 3.112 | 3.009 | 3.215 | 3.144 | 59.268 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_hyp4 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~~ extra_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_extra_ideal_hyp4 <- lavaan(mi_lcs_extra_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4427.684 | 4498.029 | 0.993 | 0.027 | 0.036 |
# parameters of interest
params_lcs_extra_ideal_hyp4 <- broom::tidy(fit_mi_lcs_extra_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.942 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_extra_1 ~1 | -0.029 | -0.079 | 0.022 | -0.095 | -1.104 | 0.270 |
| extra_t1 ~1 | 3.717 | 3.653 | 3.781 | 7.822 | 114.026 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.090 | 0.042 | 0.138 | 1.000 | 3.685 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.031 | -0.066 | 0.005 | -0.214 | -1.691 | 0.091 |
| d_extra_1 ~~ sb06_01_t1 | 0.060 | 0.008 | 0.113 | 0.203 | 2.245 | 0.025 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.979 | 0.844 | 1.115 | 1.000 | 14.197 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.007 | 3.212 | 3.142 | 59.314 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_specif_hyp4 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~~ extra_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_01_t1 ~~ sb07_01_t1
sb07_02_t1 ~~ sb07_02_t1
sb07_03_t1 ~~ sb07_03_t1
sb07_01_t1 ~ 1
sb07_02_t1 ~ 1
sb07_03_t1 ~ 1
'
fit_mi_lcs_extra_curr_specif_hyp4 <- lavaan(mi_lcs_extra_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 7117.032 | 7214.734 | 0.942 | 0.086 | 0.124 |
# parameters of interest
params_lcs_extra_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.603 | NA | NA |
| d_extra_1 ~1 | 0.133 | 0.085 | 0.181 | 0.383 | 5.435 | 0.000 |
| extra_t1 ~1 | 2.756 | 2.684 | 2.827 | 4.798 | 75.531 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.121 | 0.074 | 0.167 | 1.000 | 5.077 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.059 | -0.092 | -0.027 | -0.298 | -3.598 | 0.000 |
| goals ~~ d_extra_1 | 0.010 | -0.031 | 0.050 | 0.048 | 0.458 | 0.647 |
| goals ~~ goals | 0.330 | 0.082 | 0.578 | 1.000 | 2.604 | 0.009 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_specif_hyp4 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~~ extra_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_01_t1 ~~ sb07_01_t1
sb07_02_t1 ~~ sb07_02_t1
sb07_03_t1 ~~ sb07_03_t1
sb07_01_t1 ~ 1
sb07_02_t1 ~ 1
sb07_03_t1 ~ 1
'
fit_mi_lcs_extra_ideal_specif_hyp4 <- lavaan(mi_lcs_extra_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6795.588 | 6893.29 | 0.949 | 0.061 | 0.085 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.945 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.600 | NA | NA |
| d_extra_1 ~1 | -0.029 | -0.080 | 0.021 | -0.098 | -1.138 | 0.255 |
| extra_t1 ~1 | 3.717 | 3.653 | 3.781 | 7.849 | 114.077 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.090 | 0.041 | 0.140 | 1.000 | 3.598 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.032 | -0.069 | 0.006 | -0.223 | -1.665 | 0.096 |
| goals ~~ d_extra_1 | 0.032 | -0.034 | 0.098 | 0.173 | 0.948 | 0.343 |
| goals ~~ goals | 0.377 | 0.077 | 0.676 | 1.000 | 2.463 | 0.014 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_hyp4 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~~ agree_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_agree_curr_hyp4 <- lavaan(mi_lcs_agree_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4214.426 | 4284.771 | 0.99 | 0.037 | 0.035 |
# parameters of interest
params_lcs_agree_curr_hyp4 <- broom::tidy(fit_mi_lcs_agree_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.062 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_agree_1 ~1 | 0.076 | 0.039 | 0.114 | 0.356 | 3.968 | 0.000 |
| agree_t1 ~1 | 3.357 | 3.295 | 3.418 | 7.983 | 107.627 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.046 | 0.022 | 0.069 | 1.000 | 3.847 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.033 | -0.050 | -0.016 | -0.365 | -3.719 | 0.000 |
| d_agree_1 ~~ sb06_01_t1 | -0.014 | -0.046 | 0.019 | -0.064 | -0.805 | 0.421 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.193 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.006 | 3.212 | 3.142 | 59.219 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_hyp4 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~~ agree_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_agree_ideal_hyp4 <- lavaan(mi_lcs_agree_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4315.977 | 4386.323 | 0.999 | 0.012 | 0.031 |
# parameters of interest
params_lcs_agree_ideal_hyp4 <- broom::tidy(fit_mi_lcs_agree_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.695 | NA | NA |
| d_agree_1 ~1 | 0.032 | -0.007 | 0.070 | 0.114 | 1.617 | 0.106 |
| agree_t1 ~1 | 3.710 | 3.643 | 3.777 | 8.891 | 108.194 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.077 | 0.040 | 0.115 | 1.000 | 4.079 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.046 | -0.072 | -0.019 | -0.392 | -3.336 | 0.001 |
| d_agree_1 ~~ sb06_01_t1 | 0.016 | -0.026 | 0.058 | 0.058 | 0.742 | 0.458 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.191 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.191 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_specif_hyp4 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~~ agree_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_04_t1 ~~ sb07_04_t1
sb07_05_t1 ~~ sb07_05_t1
sb07_06_t1 ~~ sb07_06_t1
sb07_04_t1 ~ 1
sb07_05_t1 ~ 1
sb07_06_t1 ~ 1
'
fit_mi_lcs_agree_curr_specif_hyp4 <- lavaan(mi_lcs_agree_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6728.588 | 6826.29 | 0.964 | 0.061 | 0.078 |
# parameters of interest
params_lcs_agree_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.059 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.537 | NA | NA |
| d_agree_1 ~1 | 0.076 | 0.039 | 0.114 | 0.357 | 3.974 | 0.000 |
| agree_t1 ~1 | 3.357 | 3.295 | 3.418 | 7.972 | 107.605 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.046 | 0.023 | 0.069 | 1.000 | 3.900 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.032 | -0.049 | -0.015 | -0.360 | -3.720 | 0.000 |
| goals ~~ d_agree_1 | 0.005 | -0.030 | 0.041 | 0.028 | 0.303 | 0.762 |
| goals ~~ goals | 0.825 | 0.522 | 1.129 | 1.000 | 5.327 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_specif_hyp4 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~~ agree_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_04_t1 ~~ sb07_04_t1
sb07_05_t1 ~~ sb07_05_t1
sb07_06_t1 ~~ sb07_06_t1
sb07_04_t1 ~ 1
sb07_05_t1 ~ 1
sb07_06_t1 ~ 1
'
fit_mi_lcs_agree_ideal_specif_hyp4 <- lavaan(mi_lcs_agree_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6828.144 | 6925.846 | 0.995 | 0.022 | 0.035 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.695 | NA | NA |
| d_agree_1 ~1 | 0.032 | -0.006 | 0.070 | 0.115 | 1.640 | 0.101 |
| agree_t1 ~1 | 3.710 | 3.642 | 3.777 | 8.869 | 108.172 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.078 | 0.040 | 0.115 | 1.000 | 4.080 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.046 | -0.073 | -0.019 | -0.393 | -3.352 | 0.001 |
| goals ~~ d_agree_1 | -0.028 | -0.069 | 0.012 | -0.113 | -1.368 | 0.171 |
| goals ~~ goals | 0.819 | 0.507 | 1.131 | 1.000 | 5.143 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_hyp4 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_consc_curr_hyp4 <- lavaan(mi_lcs_consc_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4446.456 | 4516.801 | 0.991 | 0.049 | 0.064 |
# parameters of interest
params_lcs_consc_curr_hyp4 <- broom::tidy(fit_mi_lcs_consc_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.505 | NA | NA |
| d_consc_1 ~1 | 0.124 | 0.075 | 0.173 | 0.342 | 4.949 | 0.000 |
| consc_t1 ~1 | 3.552 | 3.468 | 3.635 | 4.793 | 83.287 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.131 | 0.087 | 0.175 | 1.000 | 5.839 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.084 | -0.125 | -0.043 | -0.313 | -3.979 | 0.000 |
| d_consc_1 ~~ sb06_01_t1 | 0.007 | -0.039 | 0.054 | 0.021 | 0.311 | 0.756 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.268 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_hyp4 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_consc_ideal_hyp4 <- lavaan(mi_lcs_consc_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 3789.358 | 3859.704 | 0.998 | 0.018 | 0.026 |
# parameters of interest
params_lcs_consc_ideal_hyp4 <- broom::tidy(fit_mi_lcs_consc_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.781 | NA | NA |
| d_consc_1 ~1 | 0.024 | -0.023 | 0.071 | 0.071 | 0.997 | 0.319 |
| consc_t1 ~1 | 4.370 | 4.314 | 4.426 | 9.388 | 153.707 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.112 | 0.059 | 0.166 | 1.000 | 4.121 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.073 | -0.114 | -0.031 | -0.465 | -3.405 | 0.001 |
| d_consc_1 ~~ sb06_01_t1 | 0.010 | -0.036 | 0.057 | 0.032 | 0.444 | 0.657 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.237 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_specif_hyp4 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_07_t1 ~~ sb07_07_t1
sb07_08_t1 ~~ sb07_08_t1
sb07_09_t1 ~~ sb07_09_t1
sb07_07_t1 ~ 1
sb07_08_t1 ~ 1
sb07_09_t1 ~ 1
'
fit_mi_lcs_consc_curr_specif_hyp4 <- lavaan(mi_lcs_consc_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6888.664 | 6986.366 | 0.934 | 0.115 | 0.205 |
# parameters of interest
params_lcs_consc_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.051 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.520 | NA | NA |
| d_consc_1 ~1 | 0.124 | 0.075 | 0.173 | 0.338 | 4.970 | 0.000 |
| consc_t1 ~1 | 3.552 | 3.468 | 3.635 | 4.795 | 83.301 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.135 | 0.088 | 0.182 | 1.000 | 5.617 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.093 | -0.141 | -0.046 | -0.343 | -3.830 | 0.000 |
| goals ~~ d_consc_1 | -0.028 | -0.095 | 0.038 | -0.068 | -0.832 | 0.405 |
| goals ~~ goals | 1.277 | 0.977 | 1.577 | 1.000 | 8.341 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_specif_hyp4 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_07_t1 ~~ sb07_07_t1
sb07_08_t1 ~~ sb07_08_t1
sb07_09_t1 ~~ sb07_09_t1
sb07_07_t1 ~ 1
sb07_08_t1 ~ 1
sb07_09_t1 ~ 1
'
fit_mi_lcs_consc_ideal_specif_hyp4 <- lavaan(mi_lcs_consc_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6232.302 | 6330.004 | 1 | 0 | 0.024 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.781 | NA | NA |
| d_consc_1 ~1 | 0.024 | -0.023 | 0.071 | 0.071 | 0.996 | 0.319 |
| consc_t1 ~1 | 4.370 | 4.314 | 4.426 | 9.389 | 153.709 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.112 | 0.059 | 0.166 | 1.000 | 4.121 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.072 | -0.114 | -0.031 | -0.465 | -3.402 | 0.001 |
| goals ~~ d_consc_1 | -0.009 | -0.060 | 0.042 | -0.024 | -0.343 | 0.732 |
| goals ~~ goals | 1.276 | 0.976 | 1.575 | 1.000 | 8.349 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_hyp4 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_neuro_curr_hyp4 <- lavaan(mi_lcs_neuro_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4916.888 | 4987.233 | 0.984 | 0.067 | 0.09 |
# parameters of interest
params_lcs_neuro_curr_hyp4 <- broom::tidy(fit_mi_lcs_neuro_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.617 | NA | NA |
| d_neuro_1 ~1 | -0.211 | -0.272 | -0.150 | -0.433 | -6.735 | 0.00 |
| neuro_t1 ~1 | 3.491 | 3.399 | 3.582 | 4.350 | 74.590 | 0.00 |
| d_neuro_1 ~~ d_neuro_1 | 0.237 | 0.166 | 0.308 | 1.000 | 6.522 | 0.00 |
| neuro_t1 ~~ d_neuro_1 | -0.130 | -0.184 | -0.075 | -0.332 | -4.655 | 0.00 |
| d_neuro_1 ~~ sb06_01_t1 | -0.045 | -0.102 | 0.013 | -0.092 | -1.514 | 0.13 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.00 |
| sb06_01_t1 ~1 | 3.111 | 3.008 | 3.213 | 3.143 | 59.351 | 0.00 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_hyp4 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_hyp4 <- lavaan(mi_lcs_neuro_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 3943.146 | 4013.492 | 0.991 | 0.037 | 0.037 |
# parameters of interest
params_lcs_neuro_ideal_hyp4 <- broom::tidy(fit_mi_lcs_neuro_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.050 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.775 | NA | NA |
| d_neuro_1 ~1 | -0.042 | -0.086 | 0.002 | -0.127 | -1.866 | 0.062 |
| neuro_t1 ~1 | 1.657 | 1.601 | 1.713 | 3.724 | 58.132 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.108 | 0.068 | 0.148 | 1.000 | 5.269 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.063 | -0.096 | -0.031 | -0.432 | -3.791 | 0.000 |
| d_neuro_1 ~~ sb06_01_t1 | -0.029 | -0.074 | 0.016 | -0.090 | -1.267 | 0.205 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.194 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.008 | 3.213 | 3.143 | 59.261 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_specif_hyp4 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_10_t1 ~~ sb07_10_t1
sb07_11_t1 ~~ sb07_11_t1
sb07_12_t1 ~~ sb07_12_t1
sb07_10_t1 ~ 1
sb07_11_t1 ~ 1
sb07_12_t1 ~ 1
'
fit_mi_lcs_neuro_curr_specif_hyp4 <- lavaan(mi_lcs_neuro_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 7479.426 | 7577.128 | 0.929 | 0.12 | 0.207 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.087 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.706 | NA | NA |
| d_neuro_1 ~1 | -0.209 | -0.270 | -0.147 | -0.402 | -6.680 | 0.00 |
| neuro_t1 ~1 | 3.490 | 3.399 | 3.582 | 4.368 | 74.608 | 0.00 |
| d_neuro_1 ~~ d_neuro_1 | 0.269 | 0.180 | 0.358 | 1.000 | 5.926 | 0.00 |
| neuro_t1 ~~ d_neuro_1 | -0.184 | -0.254 | -0.113 | -0.443 | -5.124 | 0.00 |
| goals ~~ d_neuro_1 | 0.121 | 0.030 | 0.213 | 0.213 | 2.591 | 0.01 |
| goals ~~ goals | 1.207 | 0.881 | 1.532 | 1.000 | 7.264 | 0.00 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_specif_hyp4 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_10_t1 ~~ sb07_10_t1
sb07_11_t1 ~~ sb07_11_t1
sb07_12_t1 ~~ sb07_12_t1
sb07_10_t1 ~ 1
sb07_11_t1 ~ 1
sb07_12_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_specif_hyp4 <- lavaan(mi_lcs_neuro_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6514.105 | 6611.807 | 0.987 | 0.039 | 0.031 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.773 | NA | NA |
| d_neuro_1 ~1 | -0.042 | -0.086 | 0.002 | -0.127 | -1.862 | 0.063 |
| neuro_t1 ~1 | 1.657 | 1.601 | 1.713 | 3.730 | 58.144 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.107 | 0.068 | 0.147 | 1.000 | 5.302 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.062 | -0.095 | -0.030 | -0.429 | -3.776 | 0.000 |
| goals ~~ d_neuro_1 | 0.002 | -0.045 | 0.049 | 0.006 | 0.083 | 0.934 |
| goals ~~ goals | 1.222 | 0.897 | 1.548 | 1.000 | 7.363 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_hyp4 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_openn_curr_hyp4 <- lavaan(mi_lcs_openn_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4532.571 | 4602.916 | 0.994 | 0.034 | 0.022 |
# parameters of interest
params_lcs_openn_curr_hyp4 <- broom::tidy(fit_mi_lcs_openn_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.912 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.410 | NA | NA |
| d_openn_1 ~1 | 0.057 | 0.021 | 0.093 | 0.292 | 3.139 | 0.002 |
| openn_t1 ~1 | 3.435 | 3.367 | 3.503 | 7.894 | 98.490 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.038 | 0.018 | 0.059 | 1.000 | 3.615 | 0.000 |
| openn_t1 ~~ d_openn_1 | 0.000 | -0.016 | 0.015 | -0.001 | -0.009 | 0.993 |
| d_openn_1 ~~ sb06_01_t1 | -0.013 | -0.049 | 0.023 | -0.068 | -0.717 | 0.474 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.188 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.006 | 3.212 | 3.141 | 59.187 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_hyp4 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_openn_ideal_hyp4 <- lavaan(mi_lcs_openn_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 18 | 4523.142 | 4593.488 | 0.996 | 0.022 | 0.041 |
# parameters of interest
params_lcs_openn_ideal_hyp4 <- broom::tidy(fit_mi_lcs_openn_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.644 | NA | NA |
| d_openn_1 ~1 | 0.003 | -0.037 | 0.043 | 0.013 | 0.157 | 0.875 |
| openn_t1 ~1 | 3.739 | 3.676 | 3.801 | 9.491 | 117.327 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.063 | 0.026 | 0.099 | 1.000 | 3.350 | 0.001 |
| openn_t1 ~~ d_openn_1 | -0.033 | -0.060 | -0.006 | -0.337 | -2.425 | 0.015 |
| d_openn_1 ~~ sb06_01_t1 | -0.014 | -0.059 | 0.031 | -0.058 | -0.622 | 0.534 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.200 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.007 | 3.212 | 3.141 | 59.256 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_specif_hyp4 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_13_t1 ~~ sb07_13_t1
sb07_14_t1 ~~ sb07_14_t1
sb07_15_t1 ~~ sb07_15_t1
sb07_13_t1 ~ 1
sb07_14_t1 ~ 1
sb07_15_t1 ~ 1
'
fit_mi_lcs_openn_curr_specif_hyp4 <- lavaan(mi_lcs_openn_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6833.739 | 6931.441 | 0.96 | 0.075 | 0.107 |
# parameters of interest
params_lcs_openn_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.899 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.400 | NA | NA |
| d_openn_1 ~1 | 0.057 | 0.022 | 0.093 | 0.298 | 3.177 | 0.001 |
| openn_t1 ~1 | 3.435 | 3.367 | 3.503 | 7.931 | 98.528 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.037 | 0.017 | 0.057 | 1.000 | 3.581 | 0.000 |
| openn_t1 ~~ d_openn_1 | 0.004 | -0.012 | 0.019 | 0.046 | 0.478 | 0.633 |
| goals ~~ d_openn_1 | 0.026 | 0.003 | 0.049 | 0.225 | 2.242 | 0.025 |
| goals ~~ goals | 0.364 | 0.207 | 0.521 | 1.000 | 4.541 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_specif_hyp4 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_13_t1 ~~ sb07_13_t1
sb07_14_t1 ~~ sb07_14_t1
sb07_15_t1 ~~ sb07_15_t1
sb07_13_t1 ~ 1
sb07_14_t1 ~ 1
sb07_15_t1 ~ 1
'
fit_mi_lcs_openn_ideal_specif_hyp4 <- lavaan(mi_lcs_openn_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 25 | 6826.928 | 6924.63 | 0.99 | 0.031 | 0.047 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.649 | NA | NA |
| d_openn_1 ~1 | 0.004 | -0.036 | 0.044 | 0.015 | 0.180 | 0.857 |
| openn_t1 ~1 | 3.739 | 3.676 | 3.801 | 9.452 | 117.348 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.064 | 0.027 | 0.101 | 1.000 | 3.405 | 0.001 |
| openn_t1 ~~ d_openn_1 | -0.034 | -0.060 | -0.009 | -0.345 | -2.611 | 0.009 |
| goals ~~ d_openn_1 | 0.020 | -0.008 | 0.048 | 0.129 | 1.386 | 0.166 |
| goals ~~ goals | 0.365 | 0.208 | 0.521 | 1.000 | 4.570 | 0.000 |
Run models for all facets with a template & loop:
# create template:
facet_template <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_facet_1 ~~ ind_goal # estimates the covariance/correlation with change goal variable
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# loop across 2 BFI versions (combined pre&post current/ideal)
for (j in 5:length(bfi_versions)) {
items = paste0(bfi_versions[[j]], item_nrs)
# loop across 2 different goal operationalizations (sb06_01_t1 & sb07_XX_t1)
for (k in 1:2) {
if (k==1) {
goal_op = "sb06_01_t1"
} else{
goal_op = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
}
template_filled <- str_replace_all(facet_template,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = goal_op))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
# save to environment
if (k==1) {
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp4")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp4")), facet_model_fit))
} else{
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), facet_model_fit))
}
}
}
} Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8681.358 | 8767.336 | 0.944 | 0.078 | 0.061 |
# parameters of interest
params_lcs_socia_curr_hyp4 <- broom::tidy(fit_mi_lcs_socia_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.633 | NA | NA |
| d_socia_1 ~1 | 0.180 | 0.094 | 0.267 | 0.345 | 4.095 | 0.000 |
| socia_t1 ~1 | 2.805 | 2.681 | 2.929 | 3.344 | 44.338 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.273 | 0.163 | 0.383 | 1.000 | 4.861 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.148 | -0.227 | -0.068 | -0.337 | -3.637 | 0.000 |
| d_socia_1 ~~ sb06_01_t1 | 0.088 | 0.008 | 0.167 | 0.170 | 2.159 | 0.031 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.190 | 0.000 |
| sb06_01_t1 ~1 | 3.111 | 3.008 | 3.214 | 3.143 | 59.260 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8440.681 | 8526.659 | 0.972 | 0.041 | 0.05 |
# parameters of interest
params_lcs_socia_ideal_hyp4 <- broom::tidy(fit_mi_lcs_socia_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.968 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.495 | NA | NA |
| d_socia_1 ~1 | -0.035 | -0.088 | 0.017 | -0.151 | -1.315 | 0.188 |
| socia_t1 ~1 | 4.186 | 4.105 | 4.266 | 9.094 | 102.508 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.055 | 0.001 | 0.110 | 1.000 | 1.985 | 0.047 |
| socia_t1 ~~ d_socia_1 | -0.021 | -0.053 | 0.012 | -0.189 | -1.254 | 0.210 |
| d_socia_1 ~~ sb06_01_t1 | 0.047 | -0.006 | 0.101 | 0.203 | 1.727 | 0.084 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.006 | 3.212 | 3.141 | 59.290 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8826.141 | 8912.119 | 0.904 | 0.104 | 0.116 |
# parameters of interest
params_lcs_socia_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.044 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.667 | NA | NA |
| d_socia_1 ~1 | 0.178 | 0.092 | 0.264 | 0.334 | 4.058 | 0.000 |
| socia_t1 ~1 | 2.806 | 2.682 | 2.930 | 3.367 | 44.398 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.284 | 0.168 | 0.400 | 1.000 | 4.792 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.170 | -0.257 | -0.083 | -0.383 | -3.836 | 0.000 |
| d_socia_1 ~~ sb07_01_t1 | -0.045 | -0.137 | 0.046 | -0.071 | -0.970 | 0.332 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.450 | 1.260 | 1.641 | 1.000 | 14.932 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8579.207 | 8665.184 | 0.948 | 0.057 | 0.07 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.981 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.518 | NA | NA |
| d_socia_1 ~1 | -0.037 | -0.090 | 0.015 | -0.154 | -1.388 | 0.165 |
| socia_t1 ~1 | 4.186 | 4.106 | 4.266 | 9.134 | 102.513 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.059 | 0.003 | 0.114 | 1.000 | 2.072 | 0.038 |
| socia_t1 ~~ d_socia_1 | -0.025 | -0.058 | 0.008 | -0.226 | -1.479 | 0.139 |
| d_socia_1 ~~ sb07_01_t1 | 0.067 | 0.005 | 0.130 | 0.231 | 2.111 | 0.035 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.450 | 1.260 | 1.641 | 1.000 | 14.929 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8998.591 | 9084.569 | 0.983 | 0.035 | 0.052 |
# parameters of interest
params_lcs_asser_curr_hyp4 <- broom::tidy(fit_mi_lcs_asser_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.416 | NA | NA |
| d_asser_1 ~1 | 0.056 | 0.003 | 0.108 | 0.232 | 2.072 | 0.038 |
| asser_t1 ~1 | 3.059 | 2.958 | 3.161 | 5.366 | 59.026 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.058 | 0.008 | 0.107 | 1.000 | 2.271 | 0.023 |
| asser_t1 ~~ d_asser_1 | -0.025 | -0.061 | 0.012 | -0.180 | -1.313 | 0.189 |
| d_asser_1 ~~ sb06_01_t1 | 0.034 | -0.025 | 0.093 | 0.144 | 1.136 | 0.256 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.008 | 3.213 | 3.143 | 59.300 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8821.067 | 8907.045 | 1 | 0 | 0.036 |
# parameters of interest
params_lcs_asser_ideal_hyp4 <- broom::tidy(fit_mi_lcs_asser_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.931 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_asser_1 ~1 | -0.033 | -0.071 | 0.005 | -0.158 | -1.706 | 0.088 |
| asser_t1 ~1 | 4.068 | 3.983 | 4.153 | 13.263 | 93.642 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.044 | 0.004 | 0.084 | 1.000 | 2.140 | 0.032 |
| asser_t1 ~~ d_asser_1 | -0.015 | -0.035 | 0.006 | -0.228 | -1.411 | 0.158 |
| d_asser_1 ~~ sb06_01_t1 | 0.050 | 0.009 | 0.091 | 0.241 | 2.400 | 0.016 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.191 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.007 | 3.212 | 3.142 | 59.293 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9113.289 | 9199.267 | 0.959 | 0.055 | 0.07 |
# parameters of interest
params_lcs_asser_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.420 | NA | NA |
| d_asser_1 ~1 | 0.056 | 0.003 | 0.109 | 0.230 | 2.066 | 0.039 |
| asser_t1 ~1 | 3.059 | 2.958 | 3.161 | 5.357 | 59.032 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.059 | 0.008 | 0.109 | 1.000 | 2.281 | 0.023 |
| asser_t1 ~~ d_asser_1 | -0.026 | -0.064 | 0.012 | -0.188 | -1.358 | 0.174 |
| d_asser_1 ~~ sb07_02_t1 | 0.013 | -0.047 | 0.073 | 0.046 | 0.428 | 0.668 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.345 | 1.177 | 1.514 | 1.000 | 15.655 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8941.725 | 9027.703 | 0.942 | 0.049 | 0.066 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.931 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.635 | NA | NA |
| d_asser_1 ~1 | -0.033 | -0.072 | 0.005 | -0.158 | -1.699 | 0.089 |
| asser_t1 ~1 | 4.068 | 3.983 | 4.153 | 13.163 | 93.681 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.044 | 0.004 | 0.085 | 1.000 | 2.135 | 0.033 |
| asser_t1 ~~ d_asser_1 | -0.015 | -0.036 | 0.006 | -0.229 | -1.379 | 0.168 |
| d_asser_1 ~~ sb07_02_t1 | 0.015 | -0.027 | 0.057 | 0.061 | 0.692 | 0.489 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.345 | 1.177 | 1.514 | 1.000 | 15.655 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8985.722 | 9071.7 | 0.945 | 0.065 | 0.073 |
# parameters of interest
params_lcs_energ_curr_hyp4 <- broom::tidy(fit_mi_lcs_energ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_energ_1 ~1 | -0.091 | -0.137 | -0.046 | -0.423 | -3.958 | 0.000 |
| energ_t1 ~1 | 2.709 | 2.610 | 2.809 | 6.885 | 53.380 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.047 | 0.010 | 0.083 | 1.000 | 2.488 | 0.013 |
| energ_t1 ~~ d_energ_1 | -0.021 | -0.043 | 0.001 | -0.248 | -1.891 | 0.059 |
| d_energ_1 ~~ sb06_01_t1 | -0.018 | -0.056 | 0.020 | -0.084 | -0.933 | 0.351 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.246 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8377.079 | 8463.057 | 0.949 | 0.044 | 0.047 |
# parameters of interest
params_lcs_energ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_energ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.944 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.682 | NA | NA |
| d_energ_1 ~1 | 0.015 | -0.031 | 0.062 | 0.060 | 0.649 | 0.516 |
| energ_t1 ~1 | 2.256 | 2.148 | 2.363 | 6.274 | 41.048 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.067 | 0.004 | 0.131 | 1.000 | 2.088 | 0.037 |
| energ_t1 ~~ d_energ_1 | -0.026 | -0.066 | 0.015 | -0.276 | -1.247 | 0.213 |
| d_energ_1 ~~ sb06_01_t1 | -0.036 | -0.085 | 0.013 | -0.140 | -1.440 | 0.150 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.195 | 0.000 |
| sb06_01_t1 ~1 | 3.111 | 3.008 | 3.213 | 3.143 | 59.292 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9143.348 | 9229.325 | 0.903 | 0.088 | 0.094 |
# parameters of interest
params_lcs_energ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.010 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.572 | NA | NA |
| d_energ_1 ~1 | -0.091 | -0.136 | -0.046 | -0.410 | -3.942 | 0.000 |
| energ_t1 ~1 | 2.709 | 2.609 | 2.809 | 6.905 | 53.341 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.049 | 0.011 | 0.088 | 1.000 | 2.491 | 0.013 |
| energ_t1 ~~ d_energ_1 | -0.026 | -0.051 | -0.002 | -0.300 | -2.102 | 0.036 |
| d_energ_1 ~~ sb07_03_t1 | 0.025 | -0.019 | 0.069 | 0.092 | 1.127 | 0.260 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.526 | 1.316 | 1.735 | 1.000 | 14.279 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8536.791 | 8622.769 | 0.934 | 0.05 | 0.058 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.937 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_energ_1 ~1 | 0.016 | -0.031 | 0.063 | 0.061 | 0.651 | 0.515 |
| energ_t1 ~1 | 2.256 | 2.148 | 2.363 | 6.253 | 41.044 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.066 | 0.004 | 0.129 | 1.000 | 2.075 | 0.038 |
| energ_t1 ~~ d_energ_1 | -0.024 | -0.064 | 0.016 | -0.259 | -1.176 | 0.239 |
| d_energ_1 ~~ sb07_03_t1 | 0.025 | -0.035 | 0.085 | 0.079 | 0.818 | 0.413 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.526 | 1.316 | 1.735 | 1.000 | 14.278 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8698.681 | 8784.659 | 0.991 | 0.022 | 0.037 |
# parameters of interest
params_lcs_compa_curr_hyp4 <- broom::tidy(fit_mi_lcs_compa_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.141 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.407 | NA | NA |
| d_compa_1 ~1 | 0.105 | 0.037 | 0.172 | 0.410 | 3.047 | 0.002 |
| compa_t1 ~1 | 4.148 | 4.058 | 4.239 | 5.779 | 89.697 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.065 | -0.014 | 0.144 | 1.000 | 1.622 | 0.105 |
| compa_t1 ~~ d_compa_1 | -0.093 | -0.169 | -0.016 | -0.504 | -2.371 | 0.018 |
| d_compa_1 ~~ sb06_01_t1 | 0.038 | -0.015 | 0.090 | 0.148 | 1.390 | 0.165 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.255 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9113.329 | 9199.307 | 0.988 | 0.021 | 0.042 |
# parameters of interest
params_lcs_compa_ideal_hyp4 <- broom::tidy(fit_mi_lcs_compa_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.743 | NA | NA |
| d_compa_1 ~1 | 0.015 | -0.067 | 0.098 | 0.033 | 0.368 | 0.713 |
| compa_t1 ~1 | 4.181 | 4.086 | 4.275 | 6.066 | 86.683 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.223 | 0.090 | 0.356 | 1.000 | 3.281 | 0.001 |
| compa_t1 ~~ d_compa_1 | -0.147 | -0.252 | -0.043 | -0.453 | -2.763 | 0.006 |
| d_compa_1 ~~ sb06_01_t1 | 0.024 | -0.058 | 0.106 | 0.052 | 0.580 | 0.562 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.232 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8942.98 | 9028.958 | 0.977 | 0.036 | 0.056 |
# parameters of interest
params_lcs_compa_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.140 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.403 | NA | NA |
| d_compa_1 ~1 | 0.104 | 0.037 | 0.171 | 0.414 | 3.052 | 0.002 |
| compa_t1 ~1 | 4.149 | 4.058 | 4.239 | 5.823 | 89.775 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.063 | -0.015 | 0.141 | 1.000 | 1.594 | 0.111 |
| compa_t1 ~~ d_compa_1 | -0.090 | -0.166 | -0.014 | -0.503 | -2.324 | 0.020 |
| d_compa_1 ~~ sb07_04_t1 | -0.004 | -0.089 | 0.081 | -0.012 | -0.095 | 0.924 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.937 | 1.756 | 2.117 | 1.000 | 21.048 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9355.968 | 9441.945 | 0.978 | 0.029 | 0.048 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.088 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.747 | NA | NA |
| d_compa_1 ~1 | 0.016 | -0.066 | 0.098 | 0.033 | 0.376 | 0.707 |
| compa_t1 ~1 | 4.181 | 4.086 | 4.275 | 6.064 | 86.677 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.224 | 0.093 | 0.356 | 1.000 | 3.342 | 0.001 |
| compa_t1 ~~ d_compa_1 | -0.149 | -0.252 | -0.046 | -0.456 | -2.833 | 0.005 |
| d_compa_1 ~~ sb07_04_t1 | -0.034 | -0.146 | 0.079 | -0.051 | -0.588 | 0.556 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.937 | 1.756 | 2.117 | 1.000 | 21.048 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7956.84 | 8042.818 | 0.942 | 0.067 | 0.057 |
# parameters of interest
params_lcs_respe_curr_hyp4 <- broom::tidy(fit_mi_lcs_respe_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.129 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_respe_1 ~1 | 0.065 | 0.010 | 0.120 | 0.198 | 2.328 | 0.020 |
| respe_t1 ~1 | 4.398 | 4.321 | 4.475 | 7.514 | 111.747 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.108 | 0.054 | 0.162 | 1.000 | 3.899 | 0.000 |
| respe_t1 ~~ d_respe_1 | -0.091 | -0.149 | -0.033 | -0.472 | -3.063 | 0.002 |
| d_respe_1 ~~ sb06_01_t1 | -0.035 | -0.086 | 0.016 | -0.108 | -1.351 | 0.177 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.979 | 0.844 | 1.115 | 1.000 | 14.194 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.006 | 3.212 | 3.141 | 59.230 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7424.113 | 7510.091 | 0.908 | 0.069 | 0.065 |
# parameters of interest
params_lcs_respe_ideal_hyp4 <- broom::tidy(fit_mi_lcs_respe_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.243 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.820 | NA | NA |
| d_respe_1 ~1 | 0.041 | -0.013 | 0.095 | 0.135 | 1.493 | 0.135 |
| respe_t1 ~1 | 4.618 | 4.551 | 4.686 | 10.049 | 134.737 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.092 | 0.008 | 0.176 | 1.000 | 2.144 | 0.032 |
| respe_t1 ~~ d_respe_1 | -0.083 | -0.158 | -0.008 | -0.597 | -2.178 | 0.029 |
| d_respe_1 ~~ sb06_01_t1 | 0.007 | -0.036 | 0.050 | 0.025 | 0.341 | 0.733 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.186 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8177.119 | 8263.097 | 0.891 | 0.094 | 0.104 |
# parameters of interest
params_lcs_respe_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.122 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_respe_1 ~1 | 0.065 | 0.011 | 0.120 | 0.201 | 2.340 | 0.019 |
| respe_t1 ~1 | 4.398 | 4.321 | 4.475 | 7.513 | 111.719 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.106 | 0.051 | 0.160 | 1.000 | 3.809 | 0.000 |
| respe_t1 ~~ d_respe_1 | -0.088 | -0.148 | -0.028 | -0.463 | -2.889 | 0.004 |
| d_respe_1 ~~ sb07_05_t1 | 0.011 | -0.062 | 0.085 | 0.026 | 0.304 | 0.761 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.809 | 1.604 | 2.013 | 1.000 | 17.341 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7638.682 | 7724.66 | 0.901 | 0.072 | 0.067 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.249 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.833 | NA | NA |
| d_respe_1 ~1 | 0.041 | -0.012 | 0.094 | 0.134 | 1.508 | 0.132 |
| respe_t1 ~1 | 4.618 | 4.551 | 4.686 | 10.080 | 134.848 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.093 | 0.008 | 0.178 | 1.000 | 2.154 | 0.031 |
| respe_t1 ~~ d_respe_1 | -0.084 | -0.160 | -0.009 | -0.603 | -2.195 | 0.028 |
| d_respe_1 ~~ sb07_05_t1 | -0.057 | -0.113 | 0.000 | -0.138 | -1.971 | 0.049 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.809 | 1.604 | 2.013 | 1.000 | 17.341 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9066.046 | 9152.024 | 0.959 | 0.053 | 0.041 |
# parameters of interest
params_lcs_trust_curr_hyp4 <- broom::tidy(fit_mi_lcs_trust_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.559 | NA | NA |
| d_trust_1 ~1 | -0.111 | -0.181 | -0.040 | -0.341 | -3.086 | 0.002 |
| trust_t1 ~1 | 2.876 | 2.764 | 2.988 | 4.838 | 50.258 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.105 | 0.014 | 0.196 | 1.000 | 2.269 | 0.023 |
| trust_t1 ~~ d_trust_1 | -0.061 | -0.122 | -0.001 | -0.318 | -1.976 | 0.048 |
| d_trust_1 ~~ sb06_01_t1 | -0.006 | -0.066 | 0.053 | -0.020 | -0.213 | 0.832 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.272 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8682.563 | 8768.541 | 0.972 | 0.038 | 0.043 |
# parameters of interest
params_lcs_trust_ideal_hyp4 <- broom::tidy(fit_mi_lcs_trust_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.611 | NA | NA |
| d_trust_1 ~1 | -0.020 | -0.067 | 0.026 | -0.082 | -0.852 | 0.394 |
| trust_t1 ~1 | 1.962 | 1.874 | 2.049 | 4.856 | 43.900 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.061 | 0.010 | 0.113 | 1.000 | 2.337 | 0.019 |
| trust_t1 ~~ d_trust_1 | -0.030 | -0.064 | 0.004 | -0.301 | -1.755 | 0.079 |
| d_trust_1 ~~ sb06_01_t1 | -0.016 | -0.063 | 0.030 | -0.067 | -0.697 | 0.486 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.007 | 3.212 | 3.142 | 59.310 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9284.026 | 9370.004 | 0.954 | 0.056 | 0.048 |
# parameters of interest
params_lcs_trust_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.558 | NA | NA |
| d_trust_1 ~1 | -0.111 | -0.181 | -0.040 | -0.342 | -3.076 | 0.002 |
| trust_t1 ~1 | 2.876 | 2.764 | 2.988 | 4.834 | 50.276 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.105 | 0.014 | 0.195 | 1.000 | 2.269 | 0.023 |
| trust_t1 ~~ d_trust_1 | -0.061 | -0.122 | 0.000 | -0.317 | -1.975 | 0.048 |
| d_trust_1 ~~ sb07_06_t1 | -0.003 | -0.082 | 0.077 | -0.007 | -0.071 | 0.944 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.807 | 1.641 | 1.972 | 1.000 | 21.383 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8900.72 | 8986.698 | 0.959 | 0.045 | 0.05 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.602 | NA | NA |
| d_trust_1 ~1 | -0.020 | -0.066 | 0.026 | -0.082 | -0.849 | 0.396 |
| trust_t1 ~1 | 1.961 | 1.874 | 2.049 | 4.876 | 43.903 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.060 | 0.009 | 0.111 | 1.000 | 2.305 | 0.021 |
| trust_t1 ~~ d_trust_1 | -0.028 | -0.062 | 0.005 | -0.288 | -1.648 | 0.099 |
| d_trust_1 ~~ sb07_06_t1 | 0.018 | -0.043 | 0.079 | 0.055 | 0.573 | 0.566 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.807 | 1.641 | 1.972 | 1.000 | 21.379 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8616.801 | 8702.779 | 0.977 | 0.054 | 0.046 |
# parameters of interest
params_lcs_organ_curr_hyp4 <- broom::tidy(fit_mi_lcs_organ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.535 | NA | NA |
| d_organ_1 ~1 | -0.138 | -0.217 | -0.059 | -0.269 | -3.444 | 0.001 |
| organ_t1 ~1 | 2.865 | 2.729 | 3.000 | 2.787 | 41.400 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.264 | 0.149 | 0.378 | 1.000 | 4.507 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.199 | -0.292 | -0.106 | -0.377 | -4.183 | 0.000 |
| d_organ_1 ~~ sb06_01_t1 | -0.074 | -0.153 | 0.006 | -0.145 | -1.822 | 0.068 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.845 | 1.115 | 1.000 | 14.188 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.281 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7593.783 | 7679.761 | 0.907 | 0.059 | 0.064 |
# parameters of interest
params_lcs_organ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_organ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.797 | NA | NA |
| d_organ_1 ~1 | -0.043 | -0.106 | 0.020 | -0.127 | -1.325 | 0.185 |
| organ_t1 ~1 | 1.711 | 1.613 | 1.810 | 3.854 | 34.044 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.113 | 0.042 | 0.184 | 1.000 | 3.117 | 0.002 |
| organ_t1 ~~ d_organ_1 | -0.066 | -0.120 | -0.012 | -0.443 | -2.416 | 0.016 |
| d_organ_1 ~~ sb06_01_t1 | -0.016 | -0.069 | 0.038 | -0.047 | -0.577 | 0.564 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.221 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8900.004 | 8985.981 | 0.874 | 0.132 | 0.151 |
# parameters of interest
params_lcs_organ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.112 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.586 | NA | NA |
| d_organ_1 ~1 | -0.140 | -0.218 | -0.061 | -0.258 | -3.480 | 0.001 |
| organ_t1 ~1 | 2.865 | 2.729 | 3.000 | 2.794 | 41.405 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.292 | 0.161 | 0.423 | 1.000 | 4.370 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.247 | -0.364 | -0.130 | -0.445 | -4.148 | 0.000 |
| d_organ_1 ~~ sb07_07_t1 | 0.103 | -0.032 | 0.239 | 0.130 | 1.492 | 0.136 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.162 | 1.960 | 2.364 | 1.000 | 21.025 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7874.008 | 7959.986 | 0.924 | 0.053 | 0.062 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.052 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.800 | NA | NA |
| d_organ_1 ~1 | -0.042 | -0.104 | 0.021 | -0.125 | -1.304 | 0.192 |
| organ_t1 ~1 | 1.711 | 1.612 | 1.809 | 3.898 | 34.102 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.112 | 0.042 | 0.182 | 1.000 | 3.123 | 0.002 |
| organ_t1 ~~ d_organ_1 | -0.065 | -0.118 | -0.012 | -0.443 | -2.404 | 0.016 |
| d_organ_1 ~~ sb07_07_t1 | 0.050 | -0.028 | 0.128 | 0.102 | 1.256 | 0.209 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.161 | 1.960 | 2.362 | 1.000 | 21.052 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8558.254 | 8644.231 | 0.962 | 0.06 | 0.063 |
# parameters of interest
params_lcs_produ_curr_hyp4 <- broom::tidy(fit_mi_lcs_produ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.054 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.517 | NA | NA |
| d_produ_1 ~1 | -0.152 | -0.219 | -0.085 | -0.405 | -4.459 | 0.000 |
| produ_t1 ~1 | 3.377 | 3.257 | 3.498 | 4.402 | 54.979 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.142 | 0.072 | 0.212 | 1.000 | 3.954 | 0.000 |
| produ_t1 ~~ d_produ_1 | -0.100 | -0.165 | -0.035 | -0.346 | -3.029 | 0.002 |
| d_produ_1 ~~ sb06_01_t1 | 0.023 | -0.038 | 0.084 | 0.062 | 0.739 | 0.460 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.191 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.006 | 3.212 | 3.141 | 59.257 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7122.354 | 7208.332 | 0.924 | 0.058 | 0.056 |
# parameters of interest
params_lcs_produ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_produ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.205 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.878 | NA | NA |
| d_produ_1 ~1 | -0.022 | -0.074 | 0.031 | -0.067 | -0.808 | 0.419 |
| produ_t1 ~1 | 1.635 | 1.554 | 1.716 | 3.697 | 39.516 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.104 | 0.029 | 0.179 | 1.000 | 2.709 | 0.007 |
| produ_t1 ~~ d_produ_1 | -0.082 | -0.136 | -0.029 | -0.578 | -3.028 | 0.002 |
| d_produ_1 ~~ sb06_01_t1 | 0.009 | -0.039 | 0.058 | 0.030 | 0.381 | 0.703 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.212 | 3.142 | 59.301 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8733.369 | 8819.347 | 0.919 | 0.091 | 0.117 |
# parameters of interest
params_lcs_produ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.539 | NA | NA |
| d_produ_1 ~1 | -0.152 | -0.219 | -0.085 | -0.395 | -4.444 | 0.000 |
| produ_t1 ~1 | 3.377 | 3.256 | 3.497 | 4.417 | 54.947 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.148 | 0.076 | 0.221 | 1.000 | 3.992 | 0.000 |
| produ_t1 ~~ d_produ_1 | -0.111 | -0.181 | -0.042 | -0.378 | -3.149 | 0.002 |
| d_produ_1 ~~ sb07_08_t1 | 0.052 | -0.030 | 0.134 | 0.106 | 1.233 | 0.218 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.606 | 1.377 | 1.836 | 1.000 | 13.719 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7298.571 | 7384.549 | 0.932 | 0.055 | 0.056 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.205 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.878 | NA | NA |
| d_produ_1 ~1 | -0.022 | -0.074 | 0.031 | -0.067 | -0.807 | 0.420 |
| produ_t1 ~1 | 1.635 | 1.554 | 1.716 | 3.694 | 39.517 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.104 | 0.029 | 0.179 | 1.000 | 2.715 | 0.007 |
| produ_t1 ~~ d_produ_1 | -0.082 | -0.135 | -0.029 | -0.578 | -3.043 | 0.002 |
| d_produ_1 ~~ sb07_08_t1 | 0.003 | -0.051 | 0.058 | 0.008 | 0.123 | 0.903 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.606 | 1.377 | 1.836 | 1.000 | 13.720 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8688.187 | 8774.165 | 0.91 | 0.082 | 0.086 |
# parameters of interest
params_lcs_respo_curr_hyp4 <- broom::tidy(fit_mi_lcs_respo_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.472 | NA | NA |
| d_respo_1 ~1 | 0.042 | -0.004 | 0.088 | 0.199 | 1.775 | 0.076 |
| respo_t1 ~1 | 3.623 | 3.528 | 3.719 | 8.224 | 74.304 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.044 | 0.004 | 0.084 | 1.000 | 2.169 | 0.030 |
| respo_t1 ~~ d_respo_1 | -0.020 | -0.046 | 0.005 | -0.220 | -1.568 | 0.117 |
| d_respo_1 ~~ sb06_01_t1 | -0.038 | -0.080 | 0.004 | -0.182 | -1.757 | 0.079 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.008 | 3.213 | 3.143 | 59.282 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8006.85 | 8092.828 | 0.979 | 0.03 | 0.041 |
# parameters of interest
params_lcs_respo_ideal_hyp4 <- broom::tidy(fit_mi_lcs_respo_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.004 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.609 | NA | NA |
| d_respo_1 ~1 | -0.018 | -0.077 | 0.040 | -0.066 | -0.614 | 0.539 |
| respo_t1 ~1 | 4.231 | 4.128 | 4.335 | 9.211 | 80.245 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.078 | 0.026 | 0.130 | 1.000 | 2.938 | 0.003 |
| respo_t1 ~~ d_respo_1 | -0.040 | -0.083 | 0.003 | -0.310 | -1.806 | 0.071 |
| d_respo_1 ~~ sb06_01_t1 | 0.019 | -0.035 | 0.074 | 0.070 | 0.700 | 0.484 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.190 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.240 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8981.854 | 9067.831 | 0.86 | 0.105 | 0.118 |
# parameters of interest
params_lcs_respo_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.500 | NA | NA |
| d_respo_1 ~1 | 0.042 | -0.005 | 0.088 | 0.191 | 1.764 | 0.078 |
| respo_t1 ~1 | 3.623 | 3.528 | 3.719 | 8.191 | 74.281 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.048 | 0.006 | 0.090 | 1.000 | 2.217 | 0.027 |
| respo_t1 ~~ d_respo_1 | -0.026 | -0.055 | 0.002 | -0.273 | -1.811 | 0.070 |
| d_respo_1 ~~ sb07_09_t1 | -0.061 | -0.126 | 0.004 | -0.186 | -1.826 | 0.068 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.235 | 2.060 | 2.410 | 1.000 | 25.004 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8300.929 | 8386.907 | 0.986 | 0.025 | 0.041 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.004 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.612 | NA | NA |
| d_respo_1 ~1 | -0.018 | -0.077 | 0.040 | -0.066 | -0.613 | 0.540 |
| respo_t1 ~1 | 4.231 | 4.128 | 4.335 | 9.315 | 80.295 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.077 | 0.026 | 0.128 | 1.000 | 2.939 | 0.003 |
| respo_t1 ~~ d_respo_1 | -0.039 | -0.082 | 0.003 | -0.311 | -1.803 | 0.071 |
| d_respo_1 ~~ sb07_09_t1 | 0.010 | -0.069 | 0.090 | 0.025 | 0.252 | 0.801 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.235 | 2.060 | 2.410 | 1.000 | 25.008 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9015.891 | 9101.869 | 0.967 | 0.051 | 0.062 |
# parameters of interest
params_lcs_anxie_curr_hyp4 <- broom::tidy(fit_mi_lcs_anxie_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.062 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.661 | NA | NA |
| d_anxie_1 ~1 | 0.217 | 0.126 | 0.309 | 0.382 | 4.663 | 0.000 |
| anxie_t1 ~1 | 2.842 | 2.716 | 2.967 | 3.111 | 44.418 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.324 | 0.150 | 0.497 | 1.000 | 3.658 | 0.000 |
| anxie_t1 ~~ d_anxie_1 | -0.209 | -0.326 | -0.092 | -0.402 | -3.496 | 0.000 |
| d_anxie_1 ~~ sb06_01_t1 | 0.070 | -0.008 | 0.149 | 0.125 | 1.763 | 0.078 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.191 | 0.000 |
| sb06_01_t1 ~1 | 3.111 | 3.008 | 3.213 | 3.143 | 59.292 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8708.076 | 8794.053 | 1 | 0 | 0.043 |
# parameters of interest
params_lcs_anxie_ideal_hyp4 <- broom::tidy(fit_mi_lcs_anxie_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.182 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.861 | NA | NA |
| d_anxie_1 ~1 | 0.038 | -0.004 | 0.080 | 0.169 | 1.757 | 0.079 |
| anxie_t1 ~1 | 4.553 | 4.487 | 4.619 | 14.754 | 135.065 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.051 | -0.002 | 0.103 | 1.000 | 1.880 | 0.060 |
| anxie_t1 ~~ d_anxie_1 | -0.039 | -0.080 | 0.003 | -0.559 | -1.825 | 0.068 |
| d_anxie_1 ~~ sb06_01_t1 | 0.003 | -0.031 | 0.038 | 0.015 | 0.185 | 0.853 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.212 | 3.142 | 59.271 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9306.853 | 9392.831 | 0.864 | 0.111 | 0.133 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.152 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.801 | NA | NA |
| d_anxie_1 ~1 | 0.213 | 0.122 | 0.304 | 0.337 | 4.588 | 0.000 |
| anxie_t1 ~1 | 2.842 | 2.717 | 2.968 | 3.122 | 44.470 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.401 | 0.192 | 0.610 | 1.000 | 3.768 | 0.000 |
| anxie_t1 ~~ d_anxie_1 | -0.303 | -0.444 | -0.161 | -0.525 | -4.196 | 0.000 |
| d_anxie_1 ~~ sb07_10_t1 | -0.233 | -0.381 | -0.085 | -0.244 | -3.083 | 0.002 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.271 | 2.044 | 2.498 | 1.000 | 19.605 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9007.291 | 9093.269 | 0.974 | 0.028 | 0.049 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.180 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.858 | NA | NA |
| d_anxie_1 ~1 | 0.038 | -0.004 | 0.081 | 0.169 | 1.754 | 0.079 |
| anxie_t1 ~1 | 4.553 | 4.487 | 4.619 | 14.725 | 135.023 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.051 | -0.002 | 0.103 | 1.000 | 1.881 | 0.060 |
| anxie_t1 ~~ d_anxie_1 | -0.039 | -0.080 | 0.003 | -0.557 | -1.827 | 0.068 |
| d_anxie_1 ~~ sb07_10_t1 | 0.010 | -0.035 | 0.056 | 0.031 | 0.454 | 0.650 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.271 | 2.044 | 2.498 | 1.000 | 19.605 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8893.125 | 8979.103 | 0.928 | 0.094 | 0.097 |
# parameters of interest
params_lcs_depre_curr_hyp4 <- broom::tidy(fit_mi_lcs_depre_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.968 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.564 | NA | NA |
| d_depre_1 ~1 | 0.150 | 0.088 | 0.212 | 0.413 | 4.750 | 0.000 |
| depre_t1 ~1 | 2.964 | 2.851 | 3.076 | 4.763 | 51.528 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.132 | 0.072 | 0.191 | 1.000 | 4.326 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.053 | -0.088 | -0.017 | -0.233 | -2.918 | 0.004 |
| d_depre_1 ~~ sb06_01_t1 | 0.025 | -0.026 | 0.075 | 0.068 | 0.956 | 0.339 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.008 | 3.213 | 3.143 | 59.299 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7117.271 | 7203.249 | 0.947 | 0.051 | 0.052 |
# parameters of interest
params_lcs_depre_ideal_hyp4 <- broom::tidy(fit_mi_lcs_depre_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.068 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.828 | NA | NA |
| d_depre_1 ~1 | 0.027 | -0.024 | 0.077 | 0.082 | 1.041 | 0.298 |
| depre_t1 ~1 | 4.408 | 4.337 | 4.480 | 10.523 | 121.211 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.105 | 0.046 | 0.165 | 1.000 | 3.465 | 0.001 |
| depre_t1 ~~ d_depre_1 | -0.064 | -0.112 | -0.016 | -0.468 | -2.597 | 0.009 |
| d_depre_1 ~~ sb06_01_t1 | 0.035 | -0.014 | 0.085 | 0.110 | 1.401 | 0.161 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.979 | 0.844 | 1.115 | 1.000 | 14.196 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.008 | 3.213 | 3.143 | 59.290 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9213.747 | 9299.724 | 0.853 | 0.141 | 0.163 |
# parameters of interest
params_lcs_depre_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.063 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.701 | NA | NA |
| d_depre_1 ~1 | 0.148 | 0.086 | 0.211 | 0.363 | 4.674 | 0.000 |
| depre_t1 ~1 | 2.964 | 2.851 | 3.077 | 4.777 | 51.556 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.167 | 0.089 | 0.245 | 1.000 | 4.211 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.106 | -0.160 | -0.051 | -0.417 | -3.812 | 0.000 |
| d_depre_1 ~~ sb07_11_t1 | -0.182 | -0.299 | -0.065 | -0.282 | -3.059 | 0.002 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.490 | 2.292 | 2.689 | 1.000 | 24.587 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 7451.024 | 7537.002 | 0.955 | 0.047 | 0.049 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.064 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.819 | NA | NA |
| d_depre_1 ~1 | 0.026 | -0.024 | 0.077 | 0.082 | 1.023 | 0.306 |
| depre_t1 ~1 | 4.408 | 4.337 | 4.480 | 10.446 | 121.130 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.105 | 0.045 | 0.166 | 1.000 | 3.422 | 0.001 |
| depre_t1 ~~ d_depre_1 | -0.063 | -0.112 | -0.014 | -0.460 | -2.534 | 0.011 |
| d_depre_1 ~~ sb07_11_t1 | -0.032 | -0.097 | 0.032 | -0.063 | -0.978 | 0.328 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.489 | 2.291 | 2.688 | 1.000 | 24.582 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9005.976 | 9091.954 | 0.977 | 0.049 | 0.057 |
# parameters of interest
params_lcs_volat_curr_hyp4 <- broom::tidy(fit_mi_lcs_volat_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.059 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.632 | NA | NA |
| d_volat_1 ~1 | -0.174 | -0.251 | -0.097 | -0.334 | -4.434 | 0.000 |
| volat_t1 ~1 | 3.229 | 3.107 | 3.351 | 3.700 | 51.914 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.271 | 0.180 | 0.362 | 1.000 | 5.840 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.177 | -0.248 | -0.106 | -0.389 | -4.888 | 0.000 |
| d_volat_1 ~~ sb06_01_t1 | 0.012 | -0.065 | 0.089 | 0.023 | 0.308 | 0.758 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.212 | 3.142 | 59.301 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8127.244 | 8213.222 | 0.93 | 0.053 | 0.056 |
# parameters of interest
params_lcs_volat_ideal_hyp4 <- broom::tidy(fit_mi_lcs_volat_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.944 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.589 | NA | NA |
| d_volat_1 ~1 | -0.008 | -0.072 | 0.056 | -0.027 | -0.253 | 0.800 |
| volat_t1 ~1 | 1.709 | 1.625 | 1.793 | 3.514 | 39.911 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.092 | 0.024 | 0.160 | 1.000 | 2.659 | 0.008 |
| volat_t1 ~~ d_volat_1 | -0.032 | -0.078 | 0.015 | -0.215 | -1.335 | 0.182 |
| d_volat_1 ~~ sb06_01_t1 | -0.068 | -0.129 | -0.007 | -0.226 | -2.172 | 0.030 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.979 | 0.844 | 1.115 | 1.000 | 14.193 | 0.000 |
| sb06_01_t1 ~1 | 3.111 | 3.008 | 3.214 | 3.143 | 59.298 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9211.122 | 9297.099 | 0.959 | 0.066 | 0.085 |
# parameters of interest
params_lcs_volat_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.061 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_volat_1 ~1 | -0.174 | -0.251 | -0.097 | -0.333 | -4.439 | 0.000 |
| volat_t1 ~1 | 3.229 | 3.107 | 3.351 | 3.699 | 51.918 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.272 | 0.177 | 0.367 | 1.000 | 5.627 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.179 | -0.254 | -0.103 | -0.392 | -4.638 | 0.000 |
| d_volat_1 ~~ sb07_12_t1 | 0.017 | -0.089 | 0.124 | 0.025 | 0.321 | 0.748 |
| sb07_12_t1 ~~ sb07_12_t1 | 1.743 | 1.565 | 1.921 | 1.000 | 19.222 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8337.934 | 8423.912 | 0.909 | 0.061 | 0.061 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.942 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.588 | NA | NA |
| d_volat_1 ~1 | -0.008 | -0.072 | 0.056 | -0.026 | -0.246 | 0.806 |
| volat_t1 ~1 | 1.709 | 1.625 | 1.792 | 3.531 | 39.958 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.091 | 0.024 | 0.159 | 1.000 | 2.645 | 0.008 |
| volat_t1 ~~ d_volat_1 | -0.031 | -0.078 | 0.016 | -0.210 | -1.276 | 0.202 |
| d_volat_1 ~~ sb07_12_t1 | -0.005 | -0.080 | 0.070 | -0.012 | -0.123 | 0.902 |
| sb07_12_t1 ~~ sb07_12_t1 | 1.743 | 1.565 | 1.921 | 1.000 | 19.222 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8762.021 | 8847.999 | 0.974 | 0.04 | 0.048 |
# parameters of interest
params_lcs_curio_curr_hyp4 <- broom::tidy(fit_mi_lcs_curio_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.992 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.411 | NA | NA |
| d_curio_1 ~1 | 0.064 | 0.012 | 0.115 | 0.325 | 2.417 | 0.016 |
| curio_t1 ~1 | 4.062 | 3.970 | 4.154 | 8.620 | 86.648 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.038 | -0.009 | 0.085 | 1.000 | 1.591 | 0.112 |
| curio_t1 ~~ d_curio_1 | -0.017 | -0.048 | 0.013 | -0.187 | -1.105 | 0.269 |
| d_curio_1 ~~ sb06_01_t1 | -0.002 | -0.053 | 0.050 | -0.009 | -0.070 | 0.944 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.212 | 3.142 | 59.192 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8790.523 | 8876.501 | 0.987 | 0.022 | 0.04 |
# parameters of interest
params_lcs_curio_ideal_hyp4 <- broom::tidy(fit_mi_lcs_curio_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.011 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.069 | NA | NA |
| d_curio_1 ~1 | 0.002 | -0.040 | 0.045 | 0.094 | 0.106 | 0.915 |
| curio_t1 ~1 | 4.434 | 4.362 | 4.506 | 12.307 | 120.992 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.001 | -0.036 | 0.037 | 1.000 | 0.032 | 0.974 |
| curio_t1 ~~ d_curio_1 | -0.002 | -0.027 | 0.024 | -0.191 | -0.129 | 0.897 |
| d_curio_1 ~~ sb06_01_t1 | 0.009 | -0.037 | 0.054 | 0.352 | 0.368 | 0.713 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.189 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.264 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8927.39 | 9013.368 | 0.95 | 0.055 | 0.066 |
# parameters of interest
params_lcs_curio_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.999 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.424 | NA | NA |
| d_curio_1 ~1 | 0.063 | 0.011 | 0.114 | 0.316 | 2.384 | 0.017 |
| curio_t1 ~1 | 4.062 | 3.970 | 4.154 | 8.706 | 86.704 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.039 | -0.009 | 0.087 | 1.000 | 1.611 | 0.107 |
| curio_t1 ~~ d_curio_1 | -0.019 | -0.051 | 0.012 | -0.210 | -1.207 | 0.227 |
| d_curio_1 ~~ sb07_13_t1 | -0.028 | -0.098 | 0.042 | -0.114 | -0.790 | 0.429 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.563 | 1.401 | 1.724 | 1.000 | 18.980 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8956.919 | 9042.897 | 0.977 | 0.029 | 0.043 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.009 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.052 | NA | NA |
| d_curio_1 ~1 | 0.002 | -0.040 | 0.045 | 0.130 | 0.111 | 0.912 |
| curio_t1 ~1 | 4.434 | 4.362 | 4.506 | 12.250 | 120.804 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.000 | -0.036 | 0.037 | 1.000 | 0.019 | 0.985 |
| curio_t1 ~~ d_curio_1 | -0.001 | -0.027 | 0.024 | -0.191 | -0.099 | 0.921 |
| d_curio_1 ~~ sb07_13_t1 | 0.005 | -0.057 | 0.068 | 0.227 | 0.166 | 0.868 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.563 | 1.401 | 1.724 | 1.000 | 18.985 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_hyp4 <- '
aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1
aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)
aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1
d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2
aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0
aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0
d_aesth_1 ~ 1 # This estimates the intercept of the change score
aesth_t1 ~ 1 # This estimates the intercept of aesth_t1
d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores
aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1
aesth_t1 ~~ d_aesth_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_aesth_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
bf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2
bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2
bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2
bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2
bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1
bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1
bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1
bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1
bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2
bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2
bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2
bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2
bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1
bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1
bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1
bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2
bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2
bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_aesth_curr_hyp4 <- lavaan(mi_lcs_aesth_curr_hyp4,
data=df_sbsa_wide_pers_sb %>% filter(!is.na(bf05_05_t1) & !is.na(bf05_05_t2)),
estimator='mlr', fixed.x=FALSE, missing="fiml")
# This model did not converge properly with FIML and missings. No problem with complete data, though.
summary(fit_mi_lcs_aesth_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 216 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 32
Number of equality constraints 10
Number of observations 331
Number of missing patterns 2
Model Test User Model:
Standard Scaled
Test Statistic 36.010 32.687
Degrees of freedom 32 32
P-value (Chi-square) 0.286 0.433
Scaling correction factor 1.102
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1057.895 812.052
Degrees of freedom 36 36
P-value 0.000 0.000
Scaling correction factor 1.303
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.996 0.999
Tucker-Lewis Index (TLI) 0.996 0.999
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4220.689 -4220.689
Scaling correction factor 0.861
for the MLR correction
Loglikelihood unrestricted model (H1) -4202.684 -4202.684
Scaling correction factor 1.163
for the MLR correction
Akaike (AIC) 8485.379 8485.379
Bayesian (BIC) 8569.025 8569.025
Sample-size adjusted Bayesian (SABIC) 8499.241 8499.241
Root Mean Square Error of Approximation:
RMSEA 0.019 0.008
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.047 0.041
P-value H_0: RMSEA <= 0.050 0.972 0.991
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.005
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.044
P-value H_0: Robust RMSEA <= 0.050 0.981
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.036 0.036
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 =~
b05_05_ 1.000 0.038 0.029
b05_20_ (lmb2) 24.165 1.240 19.485 0.000 0.925 0.834
b05_35_ (lmb3) 22.385 1.240 18.052 0.000 0.857 0.803
b05_50_ (lmb4) -18.358 1.335 -13.748 0.000 -0.703 -0.516
aesth_t2 =~
b05_05_ 1.000 0.039 0.030
b05_20_ (lmb2) 24.165 1.240 19.485 0.000 0.951 0.840
b05_35_ (lmb3) 22.385 1.240 18.052 0.000 0.881 0.810
b05_50_ (lmb4) -18.358 1.335 -13.748 0.000 -0.722 -0.526
d_aesth_1 =~
asth_t2 1.000 0.583 0.583
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t2 ~
aesth_t1 1.000 0.973 0.973
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 ~~
d_aesth_1 -0.000 0.000 -2.595 0.009 -0.253 -0.253
d_aesth_1 ~~
sb06_01_t1 -0.002 0.002 -0.927 0.354 -0.078 -0.079
.bf05_05_t1 ~~
.bf05_05_t2 0.550 0.106 5.209 0.000 0.550 0.316
.bf05_20_t1 ~~
.bf05_20_t2 0.108 0.069 1.547 0.122 0.108 0.286
.bf05_35_t1 ~~
.bf05_35_t2 0.108 0.061 1.792 0.073 0.108 0.268
.bf05_50_t1 ~~
.bf05_50_t2 0.820 0.098 8.324 0.000 0.820 0.603
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t2 0.000 0.000 0.000
d_aesth_1 0.002 0.002 0.918 0.358 0.069 0.069
aesth_t1 2.978 0.059 50.604 0.000 77.801 77.801
.bf05_05_1 0.000 0.000 0.000
.bf05_20_1 (m2) -68.134 3.474 -19.610 0.000 -68.134 -61.398
.bf05_35_1 (m3) -62.822 3.380 -18.587 0.000 -62.822 -58.851
.bf05_50_1 (m4) 57.238 3.970 14.419 0.000 57.238 42.037
.bf05_05_2 0.000 0.000 0.000
.bf05_20_2 (m2) -68.134 3.474 -19.610 0.000 -68.134 -60.237
.bf05_35_2 (m3) -62.822 3.380 -18.587 0.000 -62.822 -57.817
.bf05_50_2 (m4) 57.238 3.970 14.419 0.000 57.238 41.726
sb06_01_1 3.107 0.055 56.725 0.000 3.107 3.139
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.asth_t2 0.000 0.000 0.000
d_sth_1 0.001 0.000 3.860 0.000 1.000 1.000
asth_t1 0.001 0.000 9.742 0.000 1.000 1.000
.b05_05_ (res1) 1.742 0.070 24.782 0.000 1.742 0.999
.b05_20_ (res2) 0.376 0.076 4.931 0.000 0.376 0.305
.b05_35_ (res3) 0.405 0.059 6.871 0.000 0.405 0.356
.b05_50_ (res4) 1.360 0.099 13.728 0.000 1.360 0.734
.b05_05_ (res1) 1.742 0.070 24.782 0.000 1.742 0.999
.b05_20_ (res2) 0.376 0.076 4.931 0.000 0.376 0.294
.b05_35_ (res3) 0.405 0.059 6.871 0.000 0.405 0.343
.b05_50_ (res4) 1.360 0.099 13.728 0.000 1.360 0.723
s06_01_ 0.979 0.072 13.676 0.000 0.979 1.000# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 331 | 22 | 8485.379 | 8569.025 | 0.996 | 0.019 | 0.036 |
# parameters of interest
params_lcs_aesth_curr_hyp4 <- broom::tidy(fit_mi_lcs_aesth_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.973 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.583 | NA | NA |
| d_aesth_1 ~1 | 0.002 | -0.002 | 0.005 | 0.069 | 0.918 | 0.358 |
| aesth_t1 ~1 | 2.978 | 2.863 | 3.093 | 77.801 | 50.604 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 1.000 | 3.860 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | -0.253 | -2.595 | 0.009 |
| d_aesth_1 ~~ sb06_01_t1 | -0.002 | -0.006 | 0.002 | -0.079 | -0.927 | 0.354 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.979 | 0.839 | 1.120 | 1.000 | 13.676 | 0.000 |
| sb06_01_t1 ~1 | 3.107 | 3.000 | 3.214 | 3.139 | 56.725 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8888.161 | 8974.139 | 1 | 0 | 0.036 |
# parameters of interest
params_lcs_aesth_ideal_hyp4 <- broom::tidy(fit_mi_lcs_aesth_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.923 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_aesth_1 ~1 | -0.008 | -0.022 | 0.007 | -0.105 | -1.054 | 0.292 |
| aesth_t1 ~1 | 3.106 | 2.982 | 3.231 | 26.008 | 49.011 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.005 | -0.006 | 0.017 | 1.000 | 0.939 | 0.348 |
| aesth_t1 ~~ d_aesth_1 | -0.001 | -0.005 | 0.002 | -0.166 | -0.818 | 0.413 |
| d_aesth_1 ~~ sb06_01_t1 | -0.005 | -0.019 | 0.009 | -0.072 | -0.725 | 0.469 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.195 | 0.000 |
| sb06_01_t1 ~1 | 3.109 | 3.006 | 3.212 | 3.141 | 59.274 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal): (here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_specif_hyp4 <- '
aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1
aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)
aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1
d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2
aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0
aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0
d_aesth_1 ~ 1 # This estimates the intercept of the change score
aesth_t1 ~ 1 # This estimates the intercept of aesth_t1
d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores
aesth_t1 ~~ d_aesth_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_aesth_1 ~~ sb07_14_t1 # estimates the covariance/correlation with change goal variable
bf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2
bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2
bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2
bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2
bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1
bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1
bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1
bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1
bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2
bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2
bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2
bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2
bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1
bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1
bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1
bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2
bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2
bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb07_14_t1 ~~ sb07_14_t1
sb07_14_t1 ~ 1
'
fit_mi_lcs_aesth_curr_specif_hyp4 <- lavaan(mi_lcs_aesth_curr_specif_hyp4,
data=df_sbsa_wide_pers_sb %>% filter(!is.na(bf05_05_t1) & !is.na(bf05_05_t2)),
estimator='mlr', fixed.x=FALSE, missing="fiml")Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.# This model did not converge properly with missing data and FIML -> no problem when only using complete data
summary(fit_mi_lcs_aesth_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 99 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 31
Number of equality constraints 10
Number of observations 331
Number of missing patterns 2
Model Test User Model:
Standard Scaled
Test Statistic 389.367 351.031
Degrees of freedom 33 33
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.109
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1061.777 828.089
Degrees of freedom 36 36
P-value 0.000 0.000
Scaling correction factor 1.282
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.653 0.598
Tucker-Lewis Index (TLI) 0.621 0.562
Robust Comparative Fit Index (CFI) 0.652
Robust Tucker-Lewis Index (TLI) 0.621
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4500.501 -4500.501
Scaling correction factor 0.805
for the MLR correction
Loglikelihood unrestricted model (H1) -4305.817 -4305.817
Scaling correction factor 1.140
for the MLR correction
Akaike (AIC) 9043.002 9043.002
Bayesian (BIC) 9122.847 9122.847
Sample-size adjusted Bayesian (SABIC) 9056.234 9056.234
Root Mean Square Error of Approximation:
RMSEA 0.181 0.171
90 Percent confidence interval - lower 0.165 0.156
90 Percent confidence interval - upper 0.197 0.186
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
Robust RMSEA 0.180
90 Percent confidence interval - lower 0.164
90 Percent confidence interval - upper 0.197
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.206 0.206
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 =~
b05_05_ 1.000 0.000 0.000
b05_20_ (lmb2) 3.378 2.774 1.218 0.223 0.000 0.000
b05_35_ (lmb3) 3.589 2.970 1.208 0.227 0.000 0.000
b05_50_ (lmb4) -3.742 3.168 -1.181 0.237 -0.000 -0.000
aesth_t2 =~
b05_05_ 1.000 0.145 0.110
b05_20_ (lmb2) 3.378 2.774 1.218 0.223 0.491 0.426
b05_35_ (lmb3) 3.589 2.970 1.208 0.227 0.522 0.464
b05_50_ (lmb4) -3.742 3.168 -1.181 0.237 -0.544 -0.385
d_aesth_1 =~
asth_t2 1.000 0.837 0.837
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t2 ~
aesth_t1 1.000 0.000 0.000
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 ~~
d_aesth_1 0.003 0.005 0.606 0.545 Inf Inf
d_aesth_1 ~~
sb07_14_t1 0.030 0.033 0.900 0.368 0.244 0.179
.bf05_05_t1 ~~
.bf05_05_t2 0.546 0.105 5.191 0.000 0.546 0.315
.bf05_20_t1 ~~
.bf05_20_t2 0.733 0.095 7.691 0.000 0.733 0.674
.bf05_35_t1 ~~
.bf05_35_t2 0.637 0.084 7.618 0.000 0.637 0.642
.bf05_50_t1 ~~
.bf05_50_t2 1.183 0.107 11.100 0.000 1.183 0.694
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t2 0.000 0.000 0.000
d_aesth_1 0.006 0.011 0.536 0.592 0.048 0.048
aesth_t1 2.976 0.059 50.612 0.000 Inf Inf
.bf05_05_1 0.000 0.000 0.000
.bf05_20_1 (m2) -6.214 8.257 -0.753 0.452 -6.214 -5.960
.bf05_35_1 (m3) -6.830 8.845 -0.772 0.440 -6.830 -6.853
.bf05_50_1 (m4) 13.699 9.438 1.451 0.147 13.699 10.494
.bf05_05_2 0.000 0.000 0.000
.bf05_20_2 (m2) -6.214 8.257 -0.753 0.452 -6.214 -5.391
.bf05_35_2 (m3) -6.830 8.845 -0.772 0.440 -6.830 -6.070
.bf05_50_2 (m4) 13.699 9.438 1.451 0.147 13.699 9.685
sb07_14_1 2.731 0.076 36.079 0.000 2.731 1.999
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.asth_t2 0.000 0.000 0.000
d_sth_1 0.015 0.026 0.574 0.566 1.000 1.000
.b05_05_ (res1) 1.732 0.071 24.280 0.000 1.732 1.000
.b05_20_ (res2) 1.087 0.085 12.848 0.000 1.087 1.000
.b05_35_ (res3) 0.993 0.075 13.189 0.000 0.993 1.000
.b05_50_ (res4) 1.704 0.089 19.045 0.000 1.704 1.000
.b05_05_ (res1) 1.732 0.071 24.280 0.000 1.732 0.988
.b05_20_ (res2) 1.087 0.085 12.848 0.000 1.087 0.818
.b05_35_ (res3) 0.993 0.075 13.189 0.000 0.993 0.785
.b05_50_ (res4) 1.704 0.089 19.045 0.000 1.704 0.852
s07_14_ 1.866 0.089 21.060 0.000 1.866 1.000
asth_t1 0.000 NaN NaN# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 331 | 21 | 9043.002 | 9122.847 | 0.653 | 0.181 | 0.206 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.000 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.837 | NA | NA |
| d_aesth_1 ~1 | 0.006 | -0.015 | 0.027 | 0.048 | 0.536 | 0.592 |
| aesth_t1 ~1 | 2.976 | 2.861 | 3.091 | Inf | 50.612 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.015 | -0.036 | 0.066 | 1.000 | 0.574 | 0.566 |
| aesth_t1 ~~ d_aesth_1 | 0.003 | -0.007 | 0.013 | Inf | 0.606 | 0.545 |
| d_aesth_1 ~~ sb07_14_t1 | 0.030 | -0.035 | 0.094 | 0.179 | 0.900 | 0.368 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.866 | 1.692 | 2.040 | 1.000 | 21.060 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 9109.274 | 9195.252 | 1 | 0 | 0.042 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.940 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.592 | NA | NA |
| d_aesth_1 ~1 | -0.007 | -0.021 | 0.007 | -0.094 | -1.004 | 0.315 |
| aesth_t1 ~1 | 3.107 | 2.982 | 3.231 | 25.578 | 49.021 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.006 | -0.006 | 0.018 | 1.000 | 0.953 | 0.341 |
| aesth_t1 ~~ d_aesth_1 | -0.002 | -0.006 | 0.002 | -0.211 | -0.873 | 0.383 |
| d_aesth_1 ~~ sb07_14_t1 | 0.024 | -0.008 | 0.056 | 0.232 | 1.488 | 0.137 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.864 | 1.700 | 2.028 | 1.000 | 22.299 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8564.273 | 8650.251 | 0.956 | 0.061 | 0.062 |
# parameters of interest
params_lcs_imagi_curr_hyp4 <- broom::tidy(fit_mi_lcs_imagi_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.473 | NA | NA |
| d_imagi_1 ~1 | 0.099 | 0.034 | 0.163 | 0.279 | 2.994 | 0.003 |
| imagi_t1 ~1 | 3.567 | 3.470 | 3.665 | 4.930 | 71.862 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.125 | 0.043 | 0.206 | 1.000 | 2.989 | 0.003 |
| imagi_t1 ~~ d_imagi_1 | -0.045 | -0.112 | 0.021 | -0.177 | -1.330 | 0.183 |
| d_imagi_1 ~~ sb06_01_t1 | 0.011 | -0.052 | 0.074 | 0.032 | 0.348 | 0.728 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.192 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.213 | 3.142 | 59.211 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8003.379 | 8089.357 | 0.932 | 0.053 | 0.054 |
# parameters of interest
params_lcs_imagi_ideal_hyp4 <- broom::tidy(fit_mi_lcs_imagi_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.120 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.961 | NA | NA |
| d_imagi_1 ~1 | 0.021 | -0.044 | 0.086 | 0.048 | 0.623 | 0.533 |
| imagi_t1 ~1 | 4.563 | 4.496 | 4.630 | 9.129 | 133.420 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.184 | 0.051 | 0.316 | 1.000 | 2.715 | 0.007 |
| imagi_t1 ~~ d_imagi_1 | -0.117 | -0.224 | -0.011 | -0.547 | -2.153 | 0.031 |
| d_imagi_1 ~~ sb06_01_t1 | 0.015 | -0.044 | 0.074 | 0.036 | 0.509 | 0.611 |
| sb06_01_t1 ~~ sb06_01_t1 | 0.980 | 0.844 | 1.115 | 1.000 | 14.191 | 0.000 |
| sb06_01_t1 ~1 | 3.110 | 3.007 | 3.212 | 3.142 | 59.282 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8810.987 | 8896.964 | 0.926 | 0.08 | 0.09 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.964 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.467 | NA | NA |
| d_imagi_1 ~1 | 0.099 | 0.034 | 0.164 | 0.282 | 3.002 | 0.003 |
| imagi_t1 ~1 | 3.567 | 3.470 | 3.665 | 4.928 | 71.846 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.123 | 0.042 | 0.204 | 1.000 | 2.968 | 0.003 |
| imagi_t1 ~~ d_imagi_1 | -0.042 | -0.108 | 0.024 | -0.164 | -1.237 | 0.216 |
| d_imagi_1 ~~ sb07_15_t1 | 0.034 | -0.056 | 0.124 | 0.069 | 0.740 | 0.459 |
| sb07_15_t1 ~~ sb07_15_t1 | 1.981 | 1.806 | 2.156 | 1.000 | 22.165 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 22 | 8250.768 | 8336.745 | 0.926 | 0.055 | 0.058 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.119 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.957 | NA | NA |
| d_imagi_1 ~1 | 0.021 | -0.044 | 0.086 | 0.048 | 0.621 | 0.534 |
| imagi_t1 ~1 | 4.563 | 4.496 | 4.630 | 9.149 | 133.711 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.182 | 0.051 | 0.313 | 1.000 | 2.729 | 0.006 |
| imagi_t1 ~~ d_imagi_1 | -0.116 | -0.222 | -0.010 | -0.546 | -2.151 | 0.031 |
| d_imagi_1 ~~ sb07_15_t1 | -0.011 | -0.088 | 0.066 | -0.019 | -0.288 | 0.774 |
| sb07_15_t1 ~~ sb07_15_t1 | 1.981 | 1.806 | 2.156 | 1.000 | 22.166 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp4 <- bind_rows(
#traits
params_lcs_extra_curr_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_ideal_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_curr_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_ideal_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_curr_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_ideal_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_curr_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_ideal_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_curr_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_ideal_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_curr_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_ideal_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_curr_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_socia_ideal_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_asser_curr_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_ideal_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_curr_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_asser_ideal_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_energ_curr_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_ideal_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_curr_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_energ_ideal_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_compa_curr_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_ideal_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_curr_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_compa_ideal_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_respe_curr_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_ideal_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_curr_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_respe_ideal_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_trust_curr_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_ideal_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_curr_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_trust_ideal_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_organ_curr_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_ideal_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_curr_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_organ_ideal_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_produ_curr_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_ideal_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_curr_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_produ_ideal_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_respo_curr_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_ideal_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_curr_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_respo_ideal_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_anxie_curr_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_ideal_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_curr_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_anxie_ideal_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_depre_curr_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_ideal_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_curr_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_depre_ideal_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_volat_curr_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_ideal_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_curr_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_volat_ideal_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_curio_curr_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_ideal_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_curr_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_curio_ideal_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_aesth_curr_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_ideal_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_curr_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_aesth_ideal_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_imagi_curr_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_ideal_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_curr_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
params_lcs_imagi_ideal_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[1:20, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extraversion | current | general | 0.064 | 0.187 | 2.423 | 0.015 |
| extraversion | ideal | general | 0.060 | 0.203 | 2.245 | 0.025 |
| extraversion | current | specific | 0.010 | 0.048 | 0.458 | 0.647 |
| extraversion | ideal | specific | 0.032 | 0.173 | 0.948 | 0.343 |
| agreeableness | current | general | -0.014 | -0.064 | -0.805 | 0.421 |
| agreeableness | ideal | general | 0.016 | 0.058 | 0.742 | 0.458 |
| agreeableness | current | specific | 0.005 | 0.028 | 0.303 | 0.762 |
| agreeableness | ideal | specific | -0.028 | -0.113 | -1.368 | 0.171 |
| conscientiousness | current | general | 0.007 | 0.021 | 0.311 | 0.756 |
| conscientiousness | ideal | general | 0.010 | 0.032 | 0.444 | 0.657 |
| conscientiousness | current | specific | -0.028 | -0.068 | -0.832 | 0.405 |
| conscientiousness | ideal | specific | -0.009 | -0.024 | -0.343 | 0.732 |
| neuroticism | current | general | -0.045 | -0.092 | -1.514 | 0.130 |
| neuroticism | ideal | general | -0.029 | -0.090 | -1.267 | 0.205 |
| neuroticism | current | specific | 0.121 | 0.213 | 2.591 | 0.010 |
| neuroticism | ideal | specific | 0.002 | 0.006 | 0.083 | 0.934 |
| openness | current | general | -0.013 | -0.068 | -0.717 | 0.474 |
| openness | ideal | general | -0.014 | -0.058 | -0.622 | 0.534 |
| openness | current | specific | 0.026 | 0.225 | 2.242 | 0.025 |
| openness | ideal | specific | 0.020 | 0.129 | 1.386 | 0.166 |
Results summary across the Big Five facets: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[21:80, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| sociability | current | general | 0.088 | 0.180 | 2.159 | 0.031 |
| sociability | ideal | general | 0.047 | 0.207 | 1.727 | 0.084 |
| sociability | current | specific | -0.045 | -0.077 | -0.970 | 0.332 |
| sociability | ideal | specific | 0.067 | 0.238 | 2.111 | 0.035 |
| assertiveness | current | general | 0.034 | 0.147 | 1.136 | 0.256 |
| assertiveness | ideal | general | 0.050 | 0.248 | 2.400 | 0.016 |
| assertiveness | current | specific | 0.013 | 0.047 | 0.428 | 0.668 |
| assertiveness | ideal | specific | 0.015 | 0.062 | 0.692 | 0.489 |
| energy | current | general | -0.018 | -0.087 | -0.933 | 0.351 |
| energy | ideal | general | -0.036 | -0.146 | -1.440 | 0.150 |
| energy | current | specific | 0.025 | 0.096 | 1.127 | 0.260 |
| energy | ideal | specific | 0.025 | 0.082 | 0.818 | 0.413 |
| compassion | current | general | 0.038 | 0.172 | 1.390 | 0.165 |
| compassion | ideal | general | 0.024 | 0.058 | 0.580 | 0.562 |
| compassion | current | specific | -0.004 | -0.014 | -0.095 | 0.924 |
| compassion | ideal | specific | -0.034 | -0.058 | -0.588 | 0.556 |
| respectfulness | current | general | -0.035 | -0.122 | -1.351 | 0.177 |
| respectfulness | ideal | general | 0.007 | 0.031 | 0.341 | 0.733 |
| respectfulness | current | specific | 0.011 | 0.030 | 0.304 | 0.761 |
| respectfulness | ideal | specific | -0.057 | -0.173 | -1.971 | 0.049 |
| trust | current | general | -0.006 | -0.021 | -0.213 | 0.832 |
| trust | ideal | general | -0.016 | -0.070 | -0.697 | 0.486 |
| trust | current | specific | -0.003 | -0.007 | -0.071 | 0.944 |
| trust | ideal | specific | 0.018 | 0.057 | 0.573 | 0.566 |
| organization | current | general | -0.074 | -0.157 | -1.822 | 0.068 |
| organization | ideal | general | -0.016 | -0.053 | -0.577 | 0.564 |
| organization | current | specific | 0.103 | 0.145 | 1.492 | 0.136 |
| organization | ideal | specific | 0.050 | 0.113 | 1.256 | 0.209 |
| productiveness | current | general | 0.023 | 0.066 | 0.739 | 0.460 |
| productiveness | ideal | general | 0.009 | 0.036 | 0.381 | 0.703 |
| productiveness | current | specific | 0.052 | 0.114 | 1.233 | 0.218 |
| productiveness | ideal | specific | 0.003 | 0.010 | 0.122 | 0.903 |
| responsibility | current | general | -0.038 | -0.187 | -1.757 | 0.079 |
| responsibility | ideal | general | 0.019 | 0.074 | 0.700 | 0.484 |
| responsibility | current | specific | -0.061 | -0.194 | -1.826 | 0.068 |
| responsibility | ideal | specific | 0.010 | 0.026 | 0.252 | 0.801 |
| anxiety | current | general | 0.070 | 0.136 | 1.763 | 0.078 |
| anxiety | ideal | general | 0.003 | 0.018 | 0.185 | 0.853 |
| anxiety | current | specific | -0.233 | -0.287 | -3.083 | 0.002 |
| anxiety | ideal | specific | 0.010 | 0.037 | 0.454 | 0.650 |
| depression | current | general | 0.025 | 0.070 | 0.956 | 0.339 |
| depression | ideal | general | 0.035 | 0.124 | 1.401 | 0.161 |
| depression | current | specific | -0.182 | -0.311 | -3.059 | 0.002 |
| depression | ideal | specific | -0.032 | -0.071 | -0.978 | 0.328 |
| volatility | current | general | 0.012 | 0.026 | 0.308 | 0.758 |
| volatility | ideal | general | -0.068 | -0.231 | -2.172 | 0.030 |
| volatility | current | specific | 0.017 | 0.028 | 0.321 | 0.748 |
| volatility | ideal | specific | -0.005 | -0.012 | -0.123 | 0.902 |
| curiosity | current | general | -0.002 | -0.010 | -0.070 | 0.944 |
| curiosity | ideal | general | 0.009 | 0.358 | 0.368 | 0.713 |
| curiosity | current | specific | -0.028 | -0.116 | -0.790 | 0.429 |
| curiosity | ideal | specific | 0.005 | 0.232 | 0.166 | 0.868 |
| aesthetic | current | general | -0.002 | -0.082 | -0.927 | 0.354 |
| aesthetic | ideal | general | -0.005 | -0.073 | -0.725 | 0.469 |
| aesthetic | current | specific | 0.002 | 0.144 | 1.940 | 0.052 |
| aesthetic | ideal | specific | 0.024 | 0.237 | 1.488 | 0.137 |
| imagination | current | general | 0.011 | 0.033 | 0.348 | 0.728 |
| imagination | ideal | general | 0.015 | 0.043 | 0.509 | 0.611 |
| imagination | current | specific | 0.034 | 0.070 | 0.740 | 0.459 |
| imagination | ideal | specific | -0.011 | -0.022 | -0.288 | 0.774 |
Prepare data frame for plotting:
df_table_hyp4_plot <- bind_rows(
#traits
params_lcs_extra_curr_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_ideal_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_curr_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_ideal_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_curr_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_ideal_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_curr_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_ideal_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_curr_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_ideal_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_curr_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_ideal_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_curr_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_socia_ideal_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_asser_curr_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_ideal_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_curr_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_asser_ideal_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_energ_curr_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_ideal_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_curr_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_energ_ideal_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_compa_curr_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_ideal_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_curr_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_compa_ideal_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_respe_curr_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_ideal_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_curr_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_respe_ideal_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_trust_curr_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_ideal_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_curr_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_trust_ideal_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_organ_curr_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_ideal_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_curr_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_organ_ideal_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_produ_curr_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_ideal_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_curr_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_produ_ideal_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_respo_curr_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_ideal_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_curr_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_respo_ideal_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_anxie_curr_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_ideal_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_curr_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_anxie_ideal_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_depre_curr_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_ideal_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_curr_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_depre_ideal_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_volat_curr_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_ideal_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_curr_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_volat_ideal_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_curio_curr_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_ideal_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_curr_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_curio_ideal_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_aesth_curr_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_ideal_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_curr_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_aesth_ideal_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_imagi_curr_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_ideal_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_curr_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
params_lcs_imagi_ideal_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp4_plot <- df_table_hyp4_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor(ref, levels = c("current", "ideal"), labels = c("current", "ideal"))) %>%
mutate(goal = factor(goal, levels = c("general", "specific"), labels = c("general", "specific"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp4_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + goal, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
In the self-acceptance group, there will be a correlation between acceptance goals and change in ideal-self ratings but not change in current-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sa <- df_sbsa %>%
filter(rando=="Self-Acceptance") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07")), # facet-specific change goals
sa06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sa07"), sa06_01)) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2, sa06_01_t2))
# colnames(df_sbsa_wide_pers_sa)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sa <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sa)Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_hyp5 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
extra_t1 ~~ d_extra_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_extra_curr_hyp5 <- lavaan(mi_lcs_extra_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4616.434 | 4685.722 | 0.991 | 0.046 | 0.037 |
# parameters of interest
params_lcs_extra_curr_hyp5 <- broom::tidy(fit_mi_lcs_extra_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.554 | NA | NA |
| d_extra_1 ~1 | 0.168 | 0.113 | 0.223 | 0.460 | 5.955 | 0.000 |
| extra_t1 ~1 | 2.761 | 2.679 | 2.843 | 4.041 | 65.674 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.134 | 0.081 | 0.186 | 1.000 | 4.989 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.082 | -0.125 | -0.040 | -0.329 | -3.809 | 0.000 |
| d_extra_1 ~~ sa06_01_t1 | 0.062 | 0.024 | 0.099 | 0.182 | 3.212 | 0.001 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.124 | 4.321 | 4.562 | 84.137 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_hyp5 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
extra_t1 ~~ d_extra_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_extra_ideal_hyp5 <- lavaan(mi_lcs_extra_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4054.114 | 4123.402 | 0.982 | 0.04 | 0.042 |
# parameters of interest
params_lcs_extra_ideal_hyp5 <- broom::tidy(fit_mi_lcs_extra_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.909 | NA | NA |
| d_extra_1 ~1 | -0.037 | -0.096 | 0.022 | -0.093 | -1.230 | 0.219 |
| extra_t1 ~1 | 3.713 | 3.651 | 3.775 | 7.927 | 118.212 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.157 | 0.085 | 0.229 | 1.000 | 4.288 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.093 | -0.142 | -0.044 | -0.501 | -3.739 | 0.000 |
| d_extra_1 ~~ sa06_01_t1 | 0.024 | -0.018 | 0.067 | 0.066 | 1.114 | 0.265 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 84.000 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_specif_hyp5 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
extra_t1 ~~ d_extra_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_01_t1 ~~ sa07_01_t1
sa07_02_t1 ~~ sa07_02_t1
sa07_03_t1 ~~ sa07_03_t1
sa07_01_t1 ~ 1
sa07_02_t1 ~ 1
sa07_03_t1 ~ 1
'
fit_mi_lcs_extra_curr_specif_hyp5 <- lavaan(mi_lcs_extra_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 7169.184 | 7265.417 | 0.908 | 0.119 | 0.183 |
# parameters of interest
params_lcs_extra_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.034 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.552 | NA | NA |
| d_extra_1 ~1 | 0.167 | 0.112 | 0.222 | 0.459 | 5.934 | 0.000 |
| extra_t1 ~1 | 2.761 | 2.679 | 2.843 | 4.047 | 65.718 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.133 | 0.081 | 0.184 | 1.000 | 5.028 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.081 | -0.129 | -0.034 | -0.327 | -3.360 | 0.001 |
| goals ~~ d_extra_1 | 0.017 | -0.050 | 0.083 | 0.049 | 0.496 | 0.620 |
| goals ~~ goals | 0.871 | 0.469 | 1.274 | 1.000 | 4.240 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_specif_hyp5 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
extra_t1 ~~ d_extra_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_01_t1 ~~ sa07_01_t1
sa07_02_t1 ~~ sa07_02_t1
sa07_03_t1 ~~ sa07_03_t1
sa07_01_t1 ~ 1
sa07_02_t1 ~ 1
sa07_03_t1 ~ 1
'
fit_mi_lcs_extra_ideal_specif_hyp5 <- lavaan(mi_lcs_extra_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6599.694 | 6695.927 | 0.982 | 0.034 | 0.041 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.902 | NA | NA |
| d_extra_1 ~1 | -0.038 | -0.097 | 0.021 | -0.096 | -1.262 | 0.207 |
| extra_t1 ~1 | 3.713 | 3.652 | 3.775 | 7.938 | 118.226 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.155 | 0.085 | 0.226 | 1.000 | 4.315 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.092 | -0.140 | -0.044 | -0.496 | -3.736 | 0.000 |
| goals ~~ d_extra_1 | -0.031 | -0.097 | 0.036 | -0.085 | -0.905 | 0.365 |
| goals ~~ goals | 0.847 | 0.450 | 1.243 | 1.000 | 4.189 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_hyp5 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
agree_t1 ~~ d_agree_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_agree_curr_hyp5 <- lavaan(mi_lcs_agree_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4056.968 | 4126.256 | 0.971 | 0.063 | 0.066 |
# parameters of interest
params_lcs_agree_curr_hyp5 <- broom::tidy(fit_mi_lcs_agree_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.034 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.563 | NA | NA |
| d_agree_1 ~1 | 0.043 | 0.007 | 0.079 | 0.198 | 2.363 | 0.018 |
| agree_t1 ~1 | 3.331 | 3.266 | 3.396 | 8.365 | 101.075 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.047 | 0.009 | 0.085 | 1.000 | 2.434 | 0.015 |
| agree_t1 ~~ d_agree_1 | -0.029 | -0.060 | 0.003 | -0.331 | -1.795 | 0.073 |
| d_agree_1 ~~ sa06_01_t1 | 0.027 | -0.008 | 0.063 | 0.137 | 1.520 | 0.129 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 84.002 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_hyp5 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
agree_t1 ~~ d_agree_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_agree_ideal_hyp5 <- lavaan(mi_lcs_agree_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4069.367 | 4138.655 | 0.973 | 0.066 | 0.071 |
# parameters of interest
params_lcs_agree_ideal_hyp5 <- broom::tidy(fit_mi_lcs_agree_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.688 | NA | NA |
| d_agree_1 ~1 | -0.012 | -0.057 | 0.034 | -0.036 | -0.505 | 0.614 |
| agree_t1 ~1 | 3.705 | 3.632 | 3.779 | 7.961 | 98.659 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.103 | 0.063 | 0.143 | 1.000 | 5.004 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.051 | -0.082 | -0.020 | -0.342 | -3.223 | 0.001 |
| d_agree_1 ~~ sa06_01_t1 | 0.024 | -0.014 | 0.061 | 0.079 | 1.219 | 0.223 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.122 | 4.319 | 4.560 | 83.957 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_specif_hyp5 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
agree_t1 ~~ d_agree_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_04_t1 ~~ sa07_04_t1
sa07_05_t1 ~~ sa07_05_t1
sa07_06_t1 ~~ sa07_06_t1
sa07_04_t1 ~ 1
sa07_05_t1 ~ 1
sa07_06_t1 ~ 1
'
fit_mi_lcs_agree_curr_specif_hyp5 <- lavaan(mi_lcs_agree_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6436.356 | 6532.589 | 0.951 | 0.07 | 0.087 |
# parameters of interest
params_lcs_agree_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.537 | NA | NA |
| d_agree_1 ~1 | 0.043 | 0.007 | 0.078 | 0.201 | 2.339 | 0.019 |
| agree_t1 ~1 | 3.331 | 3.266 | 3.396 | 8.333 | 101.004 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.045 | 0.009 | 0.081 | 1.000 | 2.472 | 0.013 |
| agree_t1 ~~ d_agree_1 | -0.024 | -0.054 | 0.005 | -0.288 | -1.621 | 0.105 |
| goals ~~ d_agree_1 | 0.005 | -0.027 | 0.037 | 0.033 | 0.322 | 0.748 |
| goals ~~ goals | 0.550 | 0.292 | 0.808 | 1.000 | 4.173 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_specif_hyp5 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
agree_t1 ~~ d_agree_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_04_t1 ~~ sa07_04_t1
sa07_05_t1 ~~ sa07_05_t1
sa07_06_t1 ~~ sa07_06_t1
sa07_04_t1 ~ 1
sa07_05_t1 ~ 1
sa07_06_t1 ~ 1
'
fit_mi_lcs_agree_ideal_specif_hyp5 <- lavaan(mi_lcs_agree_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6447.067 | 6543.3 | 0.99 | 0.033 | 0.041 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.673 | NA | NA |
| d_agree_1 ~1 | -0.012 | -0.057 | 0.034 | -0.037 | -0.508 | 0.612 |
| agree_t1 ~1 | 3.705 | 3.631 | 3.779 | 7.926 | 98.587 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.102 | 0.062 | 0.141 | 1.000 | 5.002 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.048 | -0.078 | -0.018 | -0.321 | -3.134 | 0.002 |
| goals ~~ d_agree_1 | 0.008 | -0.032 | 0.047 | 0.033 | 0.383 | 0.701 |
| goals ~~ goals | 0.540 | 0.267 | 0.813 | 1.000 | 3.874 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_hyp5 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_consc_curr_hyp5 <- lavaan(mi_lcs_consc_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4342.361 | 4411.648 | 0.991 | 0.051 | 0.024 |
# parameters of interest
params_lcs_consc_curr_hyp5 <- broom::tidy(fit_mi_lcs_consc_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.008 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.475 | NA | NA |
| d_consc_1 ~1 | 0.154 | 0.105 | 0.202 | 0.438 | 6.228 | 0.000 |
| consc_t1 ~1 | 3.503 | 3.415 | 3.591 | 4.699 | 77.897 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.123 | 0.074 | 0.173 | 1.000 | 4.909 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.066 | -0.111 | -0.021 | -0.252 | -2.894 | 0.004 |
| d_consc_1 ~~ sa06_01_t1 | 0.020 | -0.018 | 0.057 | 0.060 | 1.025 | 0.305 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.561 | 83.908 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_hyp5 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_consc_ideal_hyp5 <- lavaan(mi_lcs_consc_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 3671.897 | 3741.185 | 0.985 | 0.045 | 0.058 |
# parameters of interest
params_lcs_consc_ideal_hyp5 <- broom::tidy(fit_mi_lcs_consc_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.044 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.736 | NA | NA |
| d_consc_1 ~1 | 0.018 | -0.031 | 0.066 | 0.051 | 0.706 | 0.480 |
| consc_t1 ~1 | 4.377 | 4.319 | 4.435 | 8.935 | 148.541 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.119 | 0.058 | 0.181 | 1.000 | 3.783 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.070 | -0.133 | -0.006 | -0.411 | -2.152 | 0.031 |
| d_consc_1 ~~ sa06_01_t1 | -0.006 | -0.043 | 0.030 | -0.020 | -0.344 | 0.731 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 84.038 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_specif_hyp5 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_07_t1 ~~ sa07_07_t1
sa07_08_t1 ~~ sa07_08_t1
sa07_09_t1 ~~ sa07_09_t1
sa07_07_t1 ~ 1
sa07_08_t1 ~ 1
sa07_09_t1 ~ 1
'
fit_mi_lcs_consc_curr_specif_hyp5 <- lavaan(mi_lcs_consc_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6760.546 | 6856.779 | 0.911 | 0.142 | 0.251 |
# parameters of interest
params_lcs_consc_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.075 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_consc_1 ~1 | 0.152 | 0.104 | 0.201 | 0.401 | 6.164 | 0.000 |
| consc_t1 ~1 | 3.503 | 3.415 | 3.592 | 4.691 | 77.881 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.144 | 0.079 | 0.209 | 1.000 | 4.372 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.110 | -0.172 | -0.047 | -0.387 | -3.442 | 0.001 |
| goals ~~ d_consc_1 | -0.108 | -0.190 | -0.027 | -0.256 | -2.610 | 0.009 |
| goals ~~ goals | 1.247 | 0.945 | 1.548 | 1.000 | 8.109 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_specif_hyp5 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
consc_t1 ~~ d_consc_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_07_t1 ~~ sa07_07_t1
sa07_08_t1 ~~ sa07_08_t1
sa07_09_t1 ~~ sa07_09_t1
sa07_07_t1 ~ 1
sa07_08_t1 ~ 1
sa07_09_t1 ~ 1
'
fit_mi_lcs_consc_ideal_specif_hyp5 <- lavaan(mi_lcs_consc_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6096.405 | 6192.639 | 0.995 | 0.023 | 0.053 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.050 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.746 | NA | NA |
| d_consc_1 ~1 | 0.017 | -0.031 | 0.066 | 0.050 | 0.700 | 0.484 |
| consc_t1 ~1 | 4.377 | 4.319 | 4.435 | 8.916 | 148.483 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.122 | 0.059 | 0.184 | 1.000 | 3.788 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.072 | -0.135 | -0.008 | -0.421 | -2.221 | 0.026 |
| goals ~~ d_consc_1 | -0.032 | -0.080 | 0.016 | -0.083 | -1.310 | 0.190 |
| goals ~~ goals | 1.241 | 0.934 | 1.547 | 1.000 | 7.938 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_hyp5 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_neuro_curr_hyp5 <- lavaan(mi_lcs_neuro_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4741.039 | 4810.327 | 0.982 | 0.07 | 0.087 |
# parameters of interest
params_lcs_neuro_curr_hyp5 <- broom::tidy(fit_mi_lcs_neuro_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_neuro_1 ~1 | -0.184 | -0.242 | -0.125 | -0.419 | -6.184 | 0.000 |
| neuro_t1 ~1 | 3.519 | 3.426 | 3.612 | 4.620 | 74.407 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.192 | 0.132 | 0.252 | 1.000 | 6.242 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.081 | -0.136 | -0.026 | -0.243 | -2.880 | 0.004 |
| d_neuro_1 ~~ sa06_01_t1 | -0.063 | -0.117 | -0.010 | -0.157 | -2.306 | 0.021 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.182 | 0.000 |
| sa06_01_t1 ~1 | 4.223 | 4.125 | 4.321 | 4.561 | 84.184 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_hyp5 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_hyp5 <- lavaan(mi_lcs_neuro_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 3758.694 | 3827.982 | 0.977 | 0.049 | 0.053 |
# parameters of interest
params_lcs_neuro_ideal_hyp5 <- broom::tidy(fit_mi_lcs_neuro_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.939 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.783 | NA | NA |
| d_neuro_1 ~1 | 0.011 | -0.038 | 0.059 | 0.033 | 0.431 | 0.666 |
| neuro_t1 ~1 | 1.646 | 1.595 | 1.697 | 4.212 | 63.180 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.106 | 0.056 | 0.156 | 1.000 | 4.155 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.043 | -0.072 | -0.014 | -0.337 | -2.900 | 0.004 |
| d_neuro_1 ~~ sa06_01_t1 | -0.013 | -0.048 | 0.022 | -0.043 | -0.728 | 0.467 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.992 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_specif_hyp5 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_10_t1 ~~ sa07_10_t1
sa07_11_t1 ~~ sa07_11_t1
sa07_12_t1 ~~ sa07_12_t1
sa07_10_t1 ~ 1
sa07_11_t1 ~ 1
sa07_12_t1 ~ 1
'
fit_mi_lcs_neuro_curr_specif_hyp5 <- lavaan(mi_lcs_neuro_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 7235.992 | 7332.225 | 0.959 | 0.087 | 0.146 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.002 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.590 | NA | NA |
| d_neuro_1 ~1 | -0.182 | -0.240 | -0.124 | -0.406 | -6.157 | 0.000 |
| neuro_t1 ~1 | 3.519 | 3.426 | 3.612 | 4.620 | 74.388 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.201 | 0.136 | 0.266 | 1.000 | 6.044 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.102 | -0.162 | -0.042 | -0.298 | -3.339 | 0.001 |
| goals ~~ d_neuro_1 | 0.020 | -0.041 | 0.081 | 0.053 | 0.650 | 0.516 |
| goals ~~ goals | 0.722 | 0.324 | 1.121 | 1.000 | 3.551 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_specif_hyp5 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
neuro_t1 ~~ d_neuro_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_10_t1 ~~ sa07_10_t1
sa07_11_t1 ~~ sa07_11_t1
sa07_12_t1 ~~ sa07_12_t1
sa07_10_t1 ~ 1
sa07_11_t1 ~ 1
sa07_12_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_specif_hyp5 <- lavaan(mi_lcs_neuro_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6245.283 | 6341.516 | 0.972 | 0.047 | 0.049 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.939 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.780 | NA | NA |
| d_neuro_1 ~1 | 0.011 | -0.037 | 0.059 | 0.034 | 0.445 | 0.656 |
| neuro_t1 ~1 | 1.646 | 1.595 | 1.697 | 4.229 | 63.227 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.105 | 0.056 | 0.153 | 1.000 | 4.209 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.042 | -0.070 | -0.014 | -0.335 | -2.932 | 0.003 |
| goals ~~ d_neuro_1 | 0.036 | 0.006 | 0.066 | 0.140 | 2.319 | 0.020 |
| goals ~~ goals | 0.633 | 0.178 | 1.089 | 1.000 | 2.725 | 0.006 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_hyp5 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_openn_curr_hyp5 <- lavaan(mi_lcs_openn_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4328.66 | 4397.948 | 0.998 | 0.017 | 0.028 |
# parameters of interest
params_lcs_openn_curr_hyp5 <- broom::tidy(fit_mi_lcs_openn_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.057 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.599 | NA | NA |
| d_openn_1 ~1 | 0.075 | 0.031 | 0.119 | 0.247 | 3.307 | 0.001 |
| openn_t1 ~1 | 3.474 | 3.399 | 3.549 | 6.490 | 91.037 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.092 | 0.055 | 0.129 | 1.000 | 4.894 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.061 | -0.092 | -0.030 | -0.376 | -3.912 | 0.000 |
| d_openn_1 ~~ sa06_01_t1 | 0.025 | -0.011 | 0.062 | 0.090 | 1.354 | 0.176 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.192 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.560 | 83.978 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_hyp5 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_openn_ideal_hyp5 <- lavaan(mi_lcs_openn_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing="fiml")
summary(fit_mi_lcs_openn_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 18 | 4063.3 | 4132.588 | 0.965 | 0.064 | 0.057 |
# parameters of interest
params_lcs_openn_ideal_hyp5 <- broom::tidy(fit_mi_lcs_openn_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.967 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.639 | NA | NA |
| d_openn_1 ~1 | -0.013 | -0.053 | 0.027 | -0.056 | -0.646 | 0.519 |
| openn_t1 ~1 | 3.811 | 3.752 | 3.870 | 10.661 | 126.436 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.056 | 0.029 | 0.083 | 1.000 | 4.028 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.023 | -0.040 | -0.007 | -0.278 | -2.764 | 0.006 |
| d_openn_1 ~~ sa06_01_t1 | 0.001 | -0.030 | 0.033 | 0.007 | 0.088 | 0.930 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.969 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_specif_hyp5 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_13_t1 ~~ sa07_13_t1
sa07_14_t1 ~~ sa07_14_t1
sa07_15_t1 ~~ sa07_15_t1
sa07_13_t1 ~ 1
sa07_14_t1 ~ 1
sa07_15_t1 ~ 1
'
fit_mi_lcs_openn_curr_specif_hyp5 <- lavaan(mi_lcs_openn_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6661.93 | 6758.163 | 0.962 | 0.073 | 0.148 |
# parameters of interest
params_lcs_openn_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.065 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.608 | NA | NA |
| d_openn_1 ~1 | 0.075 | 0.031 | 0.120 | 0.245 | 3.306 | 0.001 |
| openn_t1 ~1 | 3.474 | 3.399 | 3.549 | 6.466 | 90.990 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.094 | 0.056 | 0.132 | 1.000 | 4.792 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.064 | -0.097 | -0.032 | -0.390 | -3.855 | 0.000 |
| goals ~~ d_openn_1 | -0.014 | -0.049 | 0.022 | -0.059 | -0.747 | 0.455 |
| goals ~~ goals | 0.561 | 0.338 | 0.783 | 1.000 | 4.945 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_specif_hyp5 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
openn_t1 ~~ d_openn_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_13_t1 ~~ sa07_13_t1
sa07_14_t1 ~~ sa07_14_t1
sa07_15_t1 ~~ sa07_15_t1
sa07_13_t1 ~ 1
sa07_14_t1 ~ 1
sa07_15_t1 ~ 1
'
fit_mi_lcs_openn_ideal_specif_hyp5 <- lavaan(mi_lcs_openn_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing = "fiml")
summary(fit_mi_lcs_openn_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 25 | 6395.366 | 6491.599 | 0.986 | 0.038 | 0.052 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.967 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.639 | NA | NA |
| d_openn_1 ~1 | -0.013 | -0.053 | 0.026 | -0.056 | -0.656 | 0.512 |
| openn_t1 ~1 | 3.811 | 3.752 | 3.870 | 10.656 | 126.434 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.056 | 0.028 | 0.083 | 1.000 | 3.988 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.024 | -0.040 | -0.007 | -0.279 | -2.788 | 0.005 |
| goals ~~ d_openn_1 | -0.003 | -0.032 | 0.026 | -0.016 | -0.193 | 0.847 |
| goals ~~ goals | 0.560 | 0.338 | 0.782 | 1.000 | 4.940 | 0.000 |
Run models for all facets with a template & loop:
# create template:
facet_template <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
d_facet_1 ~~ ind_goal # estimates the covariance/correlation with acceptance goal variable
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# loop across 2 BFI versions (combined pre&post current/ideal)
for (j in 5:length(bfi_versions)) {
items = paste0(bfi_versions[[j]], item_nrs)
# loop across 2 different goal operationalizations (sa06_01_t1 & sa07_XX_t1)
for (k in 1:2) {
if (k==1) {
goal_op = "sa06_01_t1"
} else{
goal_op = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
}
template_filled <- str_replace_all(facet_template,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = goal_op))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
# save to environment
if (k==1) {
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp5")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp5")), facet_model_fit))
} else{
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), facet_model_fit))
}
}
}
} Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8149.317 | 8234.002 | 0.974 | 0.058 | 0.048 |
# parameters of interest
params_lcs_socia_curr_hyp5 <- broom::tidy(fit_mi_lcs_socia_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.423 | NA | NA |
| d_socia_1 ~1 | 0.175 | 0.104 | 0.245 | 0.475 | 4.859 | 0.000 |
| socia_t1 ~1 | 2.825 | 2.693 | 2.956 | 3.206 | 42.175 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.135 | 0.065 | 0.205 | 1.000 | 3.788 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.077 | -0.142 | -0.012 | -0.238 | -2.334 | 0.020 |
| d_socia_1 ~~ sa06_01_t1 | 0.089 | 0.035 | 0.142 | 0.260 | 3.243 | 0.001 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.219 | 4.120 | 4.318 | 4.558 | 83.631 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8056.518 | 8141.204 | 0.966 | 0.04 | 0.048 |
# parameters of interest
params_lcs_socia_ideal_hyp5 <- broom::tidy(fit_mi_lcs_socia_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.745 | NA | NA |
| d_socia_1 ~1 | -0.058 | -0.107 | -0.009 | -0.230 | -2.342 | 0.019 |
| socia_t1 ~1 | 4.321 | 4.250 | 4.393 | 12.165 | 118.382 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.064 | 0.018 | 0.110 | 1.000 | 2.714 | 0.007 |
| socia_t1 ~~ d_socia_1 | -0.037 | -0.063 | -0.012 | -0.418 | -2.930 | 0.003 |
| d_socia_1 ~~ sa06_01_t1 | -0.013 | -0.045 | 0.020 | -0.054 | -0.767 | 0.443 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.949 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8430.179 | 8514.864 | 0.918 | 0.107 | 0.141 |
# parameters of interest
params_lcs_socia_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.023 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.434 | NA | NA |
| d_socia_1 ~1 | 0.172 | 0.102 | 0.242 | 0.463 | 4.806 | 0.000 |
| socia_t1 ~1 | 2.826 | 2.694 | 2.957 | 3.225 | 42.204 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.138 | 0.068 | 0.207 | 1.000 | 3.883 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.086 | -0.157 | -0.015 | -0.265 | -2.377 | 0.017 |
| d_socia_1 ~~ sa07_01_t1 | 0.002 | -0.089 | 0.093 | 0.004 | 0.039 | 0.969 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.904 | 1.711 | 2.098 | 1.000 | 19.269 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8327.682 | 8412.367 | 0.953 | 0.047 | 0.051 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.047 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.743 | NA | NA |
| d_socia_1 ~1 | -0.058 | -0.107 | -0.009 | -0.229 | -2.330 | 0.020 |
| socia_t1 ~1 | 4.321 | 4.250 | 4.393 | 12.038 | 118.246 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.065 | 0.018 | 0.112 | 1.000 | 2.691 | 0.007 |
| socia_t1 ~~ d_socia_1 | -0.038 | -0.063 | -0.013 | -0.416 | -2.944 | 0.003 |
| d_socia_1 ~~ sa07_01_t1 | -0.009 | -0.074 | 0.057 | -0.025 | -0.262 | 0.793 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.904 | 1.711 | 2.098 | 1.000 | 19.269 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8530.433 | 8615.119 | 0.979 | 0.044 | 0.043 |
# parameters of interest
params_lcs_asser_curr_hyp5 <- broom::tidy(fit_mi_lcs_asser_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.559 | NA | NA |
| d_asser_1 ~1 | 0.083 | 0.026 | 0.141 | 0.235 | 2.826 | 0.005 |
| asser_t1 ~1 | 3.132 | 3.019 | 3.245 | 4.798 | 54.210 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.126 | 0.067 | 0.185 | 1.000 | 4.169 | 0.000 |
| asser_t1 ~~ d_asser_1 | -0.074 | -0.120 | -0.028 | -0.320 | -3.162 | 0.002 |
| d_asser_1 ~~ sa06_01_t1 | 0.056 | 0.011 | 0.102 | 0.171 | 2.425 | 0.015 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.184 | 0.000 |
| sa06_01_t1 ~1 | 4.224 | 4.126 | 4.322 | 4.562 | 84.289 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8314.873 | 8399.558 | 0.99 | 0.021 | 0.039 |
# parameters of interest
params_lcs_asser_ideal_hyp5 <- broom::tidy(fit_mi_lcs_asser_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.015 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.754 | NA | NA |
| d_asser_1 ~1 | -0.034 | -0.071 | 0.003 | -0.167 | -1.790 | 0.074 |
| asser_t1 ~1 | 4.163 | 4.072 | 4.254 | 15.355 | 89.784 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.041 | 0.003 | 0.078 | 1.000 | 2.137 | 0.033 |
| asser_t1 ~~ d_asser_1 | -0.021 | -0.044 | 0.001 | -0.391 | -1.849 | 0.064 |
| d_asser_1 ~~ sa06_01_t1 | 0.015 | -0.014 | 0.044 | 0.082 | 1.023 | 0.306 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 84.000 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8806.119 | 8890.804 | 0.919 | 0.088 | 0.103 |
# parameters of interest
params_lcs_asser_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_asser_1 ~1 | 0.084 | 0.025 | 0.142 | 0.231 | 2.793 | 0.005 |
| asser_t1 ~1 | 3.132 | 3.018 | 3.245 | 4.759 | 54.144 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.131 | 0.068 | 0.194 | 1.000 | 4.098 | 0.000 |
| asser_t1 ~~ d_asser_1 | -0.080 | -0.131 | -0.029 | -0.336 | -3.052 | 0.002 |
| d_asser_1 ~~ sa07_02_t1 | -0.010 | -0.091 | 0.070 | -0.021 | -0.253 | 0.800 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.902 | 1.715 | 2.089 | 1.000 | 19.964 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8585.39 | 8670.075 | 0.996 | 0.013 | 0.037 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.008 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.742 | NA | NA |
| d_asser_1 ~1 | -0.034 | -0.071 | 0.003 | -0.169 | -1.789 | 0.074 |
| asser_t1 ~1 | 4.163 | 4.072 | 4.254 | 15.223 | 89.911 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.041 | 0.004 | 0.077 | 1.000 | 2.154 | 0.031 |
| asser_t1 ~~ d_asser_1 | -0.021 | -0.043 | 0.002 | -0.379 | -1.820 | 0.069 |
| d_asser_1 ~~ sa07_02_t1 | -0.021 | -0.069 | 0.026 | -0.077 | -0.893 | 0.372 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.902 | 1.715 | 2.088 | 1.000 | 19.966 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8600.005 | 8684.69 | 0.961 | 0.057 | 0.053 |
# parameters of interest
params_lcs_energ_curr_hyp5 <- broom::tidy(fit_mi_lcs_energ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.665 | NA | NA |
| d_energ_1 ~1 | -0.107 | -0.166 | -0.048 | -0.334 | -3.532 | 0.000 |
| energ_t1 ~1 | 2.686 | 2.581 | 2.792 | 5.383 | 49.714 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.103 | 0.034 | 0.171 | 1.000 | 2.933 | 0.003 |
| energ_t1 ~~ d_energ_1 | -0.060 | -0.103 | -0.017 | -0.375 | -2.724 | 0.006 |
| d_energ_1 ~~ sa06_01_t1 | -0.019 | -0.056 | 0.019 | -0.063 | -0.985 | 0.325 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.561 | 83.964 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7672.832 | 7757.517 | 0.909 | 0.05 | 0.062 |
# parameters of interest
params_lcs_energ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_energ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.109 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.941 | NA | NA |
| d_energ_1 ~1 | -0.013 | -0.059 | 0.034 | -0.053 | -0.544 | 0.587 |
| energ_t1 ~1 | 2.392 | 2.276 | 2.508 | 8.392 | 40.361 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.058 | -0.033 | 0.150 | 1.000 | 1.246 | 0.213 |
| energ_t1 ~~ d_energ_1 | -0.037 | -0.102 | 0.029 | -0.534 | -1.102 | 0.271 |
| d_energ_1 ~~ sa06_01_t1 | -0.028 | -0.064 | 0.008 | -0.124 | -1.512 | 0.130 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.562 | 84.030 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8878.247 | 8962.932 | 0.867 | 0.112 | 0.131 |
# parameters of interest
params_lcs_energ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.717 | NA | NA |
| d_energ_1 ~1 | -0.106 | -0.164 | -0.047 | -0.317 | -3.521 | 0.000 |
| energ_t1 ~1 | 2.686 | 2.580 | 2.792 | 5.394 | 49.755 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.111 | 0.036 | 0.185 | 1.000 | 2.902 | 0.004 |
| energ_t1 ~~ d_energ_1 | -0.072 | -0.124 | -0.020 | -0.432 | -2.696 | 0.007 |
| d_energ_1 ~~ sa07_03_t1 | 0.054 | -0.023 | 0.131 | 0.116 | 1.373 | 0.170 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.954 | 1.765 | 2.144 | 1.000 | 20.238 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7952.582 | 8037.267 | 0.904 | 0.052 | 0.063 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.095 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.914 | NA | NA |
| d_energ_1 ~1 | -0.011 | -0.057 | 0.034 | -0.049 | -0.493 | 0.622 |
| energ_t1 ~1 | 2.392 | 2.276 | 2.508 | 8.429 | 40.382 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.056 | -0.031 | 0.143 | 1.000 | 1.257 | 0.209 |
| energ_t1 ~~ d_energ_1 | -0.035 | -0.097 | 0.027 | -0.517 | -1.095 | 0.273 |
| d_energ_1 ~~ sa07_03_t1 | 0.042 | -0.016 | 0.099 | 0.126 | 1.420 | 0.156 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.954 | 1.765 | 2.144 | 1.000 | 20.239 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8500.155 | 8584.84 | 0.935 | 0.062 | 0.064 |
# parameters of interest
params_lcs_compa_curr_hyp5 <- broom::tidy(fit_mi_lcs_compa_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.143 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.748 | NA | NA |
| d_compa_1 ~1 | 0.086 | -0.004 | 0.175 | 0.162 | 1.873 | 0.061 |
| compa_t1 ~1 | 4.135 | 4.034 | 4.236 | 5.114 | 80.387 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.280 | 0.085 | 0.475 | 1.000 | 2.813 | 0.005 |
| compa_t1 ~~ d_compa_1 | -0.217 | -0.374 | -0.060 | -0.506 | -2.705 | 0.007 |
| d_compa_1 ~~ sa06_01_t1 | 0.060 | -0.021 | 0.141 | 0.122 | 1.452 | 0.147 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.122 | 4.319 | 4.560 | 83.971 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8700.684 | 8785.37 | 0.974 | 0.032 | 0.053 |
# parameters of interest
params_lcs_compa_ideal_hyp5 <- broom::tidy(fit_mi_lcs_compa_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.592 | NA | NA |
| d_compa_1 ~1 | -0.039 | -0.110 | 0.032 | -0.117 | -1.082 | 0.279 |
| compa_t1 ~1 | 4.255 | 4.163 | 4.347 | 7.356 | 90.728 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.113 | 0.003 | 0.222 | 1.000 | 2.020 | 0.043 |
| compa_t1 ~~ d_compa_1 | -0.063 | -0.138 | 0.012 | -0.324 | -1.641 | 0.101 |
| d_compa_1 ~~ sa06_01_t1 | 0.012 | -0.047 | 0.071 | 0.039 | 0.402 | 0.688 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.986 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8733.263 | 8817.948 | 0.927 | 0.066 | 0.068 |
# parameters of interest
params_lcs_compa_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.130 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.726 | NA | NA |
| d_compa_1 ~1 | 0.082 | -0.005 | 0.170 | 0.161 | 1.837 | 0.066 |
| compa_t1 ~1 | 4.136 | 4.036 | 4.236 | 5.205 | 80.767 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.261 | 0.082 | 0.439 | 1.000 | 2.860 | 0.004 |
| compa_t1 ~~ d_compa_1 | -0.199 | -0.344 | -0.054 | -0.490 | -2.688 | 0.007 |
| d_compa_1 ~~ sa07_04_t1 | -0.027 | -0.117 | 0.064 | -0.040 | -0.574 | 0.566 |
| sa07_04_t1 ~~ sa07_04_t1 | 1.690 | 1.511 | 1.870 | 1.000 | 18.465 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8926.681 | 9011.366 | 1 | 0 | 0.032 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_compa_1 ~1 | -0.039 | -0.110 | 0.032 | -0.117 | -1.085 | 0.278 |
| compa_t1 ~1 | 4.255 | 4.163 | 4.347 | 7.382 | 90.696 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.113 | 0.004 | 0.221 | 1.000 | 2.039 | 0.041 |
| compa_t1 ~~ d_compa_1 | -0.063 | -0.133 | 0.008 | -0.324 | -1.738 | 0.082 |
| d_compa_1 ~~ sa07_04_t1 | -0.088 | -0.176 | -0.001 | -0.203 | -1.988 | 0.047 |
| sa07_04_t1 ~~ sa07_04_t1 | 1.690 | 1.511 | 1.870 | 1.000 | 18.464 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7503.858 | 7588.543 | 0.938 | 0.07 | 0.062 |
# parameters of interest
params_lcs_respe_curr_hyp5 <- broom::tidy(fit_mi_lcs_respe_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.601 | NA | NA |
| d_respe_1 ~1 | 0.055 | 0.009 | 0.102 | 0.199 | 2.318 | 0.020 |
| respe_t1 ~1 | 4.436 | 4.365 | 4.506 | 9.393 | 122.932 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.077 | 0.029 | 0.125 | 1.000 | 3.163 | 0.002 |
| respe_t1 ~~ d_respe_1 | -0.043 | -0.080 | -0.006 | -0.327 | -2.258 | 0.024 |
| d_respe_1 ~~ sa06_01_t1 | 0.012 | -0.032 | 0.056 | 0.047 | 0.539 | 0.590 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.955 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 6977.448 | 7062.133 | 0.9 | 0.072 | 0.074 |
# parameters of interest
params_lcs_respe_ideal_hyp5 <- broom::tidy(fit_mi_lcs_respe_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.959 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.673 | NA | NA |
| d_respe_1 ~1 | 0.007 | -0.038 | 0.052 | 0.030 | 0.312 | 0.755 |
| respe_t1 ~1 | 4.672 | 4.613 | 4.731 | 13.653 | 156.301 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.058 | 0.014 | 0.101 | 1.000 | 2.601 | 0.009 |
| respe_t1 ~~ d_respe_1 | -0.024 | -0.057 | 0.010 | -0.288 | -1.389 | 0.165 |
| d_respe_1 ~~ sa06_01_t1 | 0.011 | -0.025 | 0.048 | 0.052 | 0.624 | 0.533 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.981 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7720.113 | 7804.798 | 0.906 | 0.088 | 0.099 |
# parameters of interest
params_lcs_respe_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.009 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.587 | NA | NA |
| d_respe_1 ~1 | 0.055 | 0.008 | 0.102 | 0.200 | 2.313 | 0.021 |
| respe_t1 ~1 | 4.436 | 4.365 | 4.506 | 9.386 | 122.943 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.076 | 0.029 | 0.123 | 1.000 | 3.157 | 0.002 |
| respe_t1 ~~ d_respe_1 | -0.040 | -0.078 | -0.002 | -0.307 | -2.060 | 0.039 |
| d_respe_1 ~~ sa07_05_t1 | 0.013 | -0.052 | 0.078 | 0.037 | 0.388 | 0.698 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.620 | 1.354 | 1.887 | 1.000 | 11.923 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7193.792 | 7278.477 | 0.925 | 0.061 | 0.063 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.956 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.667 | NA | NA |
| d_respe_1 ~1 | 0.007 | -0.038 | 0.052 | 0.030 | 0.309 | 0.758 |
| respe_t1 ~1 | 4.672 | 4.613 | 4.731 | 13.634 | 156.255 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.057 | 0.014 | 0.100 | 1.000 | 2.588 | 0.010 |
| respe_t1 ~~ d_respe_1 | -0.023 | -0.056 | 0.010 | -0.281 | -1.365 | 0.172 |
| d_respe_1 ~~ sa07_05_t1 | -0.009 | -0.065 | 0.047 | -0.030 | -0.315 | 0.753 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.620 | 1.354 | 1.887 | 1.000 | 11.923 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8634.336 | 8719.021 | 0.966 | 0.049 | 0.054 |
# parameters of interest
params_lcs_trust_curr_hyp5 <- broom::tidy(fit_mi_lcs_trust_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.538 | NA | NA |
| d_trust_1 ~1 | -0.083 | -0.147 | -0.019 | -0.258 | -2.532 | 0.011 |
| trust_t1 ~1 | 2.894 | 2.779 | 3.009 | 4.611 | 49.273 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.103 | 0.004 | 0.202 | 1.000 | 2.035 | 0.042 |
| trust_t1 ~~ d_trust_1 | -0.071 | -0.135 | -0.007 | -0.352 | -2.170 | 0.030 |
| d_trust_1 ~~ sa06_01_t1 | -0.019 | -0.070 | 0.032 | -0.063 | -0.717 | 0.473 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.971 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8299.244 | 8383.929 | 0.936 | 0.056 | 0.058 |
# parameters of interest
params_lcs_trust_ideal_hyp5 <- broom::tidy(fit_mi_lcs_trust_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_trust_1 ~1 | 0.029 | -0.025 | 0.084 | 0.100 | 1.050 | 0.294 |
| trust_t1 ~1 | 1.905 | 1.811 | 1.999 | 4.346 | 39.623 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.086 | 0.032 | 0.141 | 1.000 | 3.091 | 0.002 |
| trust_t1 ~~ d_trust_1 | -0.043 | -0.082 | -0.003 | -0.332 | -2.127 | 0.033 |
| d_trust_1 ~~ sa06_01_t1 | -0.045 | -0.091 | 0.002 | -0.164 | -1.867 | 0.062 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.220 | 4.121 | 4.318 | 4.559 | 83.890 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8909.551 | 8994.236 | 0.958 | 0.056 | 0.066 |
# parameters of interest
params_lcs_trust_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.057 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_trust_1 ~1 | -0.081 | -0.144 | -0.018 | -0.252 | -2.505 | 0.012 |
| trust_t1 ~1 | 2.893 | 2.778 | 3.008 | 4.669 | 49.248 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.102 | 0.007 | 0.198 | 1.000 | 2.101 | 0.036 |
| trust_t1 ~~ d_trust_1 | -0.071 | -0.133 | -0.010 | -0.360 | -2.289 | 0.022 |
| d_trust_1 ~~ sa07_06_t1 | 0.045 | -0.028 | 0.118 | 0.102 | 1.213 | 0.225 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.934 | 1.748 | 2.120 | 1.000 | 20.379 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8578.922 | 8663.607 | 0.957 | 0.046 | 0.044 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.640 | NA | NA |
| d_trust_1 ~1 | 0.030 | -0.024 | 0.084 | 0.106 | 1.081 | 0.280 |
| trust_t1 ~1 | 1.905 | 1.811 | 1.999 | 4.404 | 39.668 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.080 | 0.029 | 0.131 | 1.000 | 3.073 | 0.002 |
| trust_t1 ~~ d_trust_1 | -0.036 | -0.072 | 0.000 | -0.295 | -1.957 | 0.050 |
| d_trust_1 ~~ sa07_06_t1 | 0.010 | -0.059 | 0.078 | 0.025 | 0.281 | 0.779 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.934 | 1.748 | 2.120 | 1.000 | 20.381 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8245.77 | 8330.455 | 0.978 | 0.056 | 0.034 |
# parameters of interest
params_lcs_organ_curr_hyp5 <- broom::tidy(fit_mi_lcs_organ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.054 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.475 | NA | NA |
| d_organ_1 ~1 | -0.143 | -0.217 | -0.069 | -0.303 | -3.782 | 0.000 |
| organ_t1 ~1 | 2.958 | 2.818 | 3.099 | 2.829 | 41.274 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.223 | 0.127 | 0.318 | 1.000 | 4.548 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.165 | -0.247 | -0.084 | -0.335 | -3.994 | 0.000 |
| d_organ_1 ~~ sa06_01_t1 | -0.004 | -0.062 | 0.054 | -0.009 | -0.133 | 0.894 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.839 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7177.95 | 7262.635 | 0.918 | 0.058 | 0.06 |
# parameters of interest
params_lcs_organ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_organ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.821 | NA | NA |
| d_organ_1 ~1 | 0.001 | -0.031 | 0.033 | 0.005 | 0.064 | 0.949 |
| organ_t1 ~1 | 1.696 | 1.607 | 1.785 | 6.670 | 37.349 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.041 | 0.002 | 0.080 | 1.000 | 2.051 | 0.040 |
| organ_t1 ~~ d_organ_1 | -0.022 | -0.043 | -0.002 | -0.438 | -2.118 | 0.034 |
| d_organ_1 ~~ sa06_01_t1 | 0.007 | -0.019 | 0.032 | 0.037 | 0.528 | 0.598 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.561 | 84.126 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8573.096 | 8657.781 | 0.878 | 0.138 | 0.174 |
# parameters of interest
params_lcs_organ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.113 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.547 | NA | NA |
| d_organ_1 ~1 | -0.141 | -0.216 | -0.067 | -0.275 | -3.732 | 0.000 |
| organ_t1 ~1 | 2.958 | 2.818 | 3.099 | 2.827 | 41.273 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.264 | 0.146 | 0.382 | 1.000 | 4.396 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.237 | -0.344 | -0.130 | -0.442 | -4.349 | 0.000 |
| d_organ_1 ~~ sa07_07_t1 | 0.164 | 0.029 | 0.300 | 0.211 | 2.380 | 0.017 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.290 | 2.110 | 2.470 | 1.000 | 24.910 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7511.071 | 7595.757 | 0.926 | 0.055 | 0.06 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.041 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.837 | NA | NA |
| d_organ_1 ~1 | 0.001 | -0.031 | 0.033 | 0.005 | 0.063 | 0.950 |
| organ_t1 ~1 | 1.696 | 1.607 | 1.785 | 6.657 | 37.345 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.042 | 0.002 | 0.082 | 1.000 | 2.053 | 0.040 |
| organ_t1 ~~ d_organ_1 | -0.023 | -0.045 | -0.002 | -0.450 | -2.132 | 0.033 |
| d_organ_1 ~~ sa07_07_t1 | 0.015 | -0.027 | 0.058 | 0.050 | 0.704 | 0.481 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.290 | 2.110 | 2.470 | 1.000 | 24.902 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8282.901 | 8367.586 | 0.985 | 0.04 | 0.038 |
# parameters of interest
params_lcs_produ_curr_hyp5 <- broom::tidy(fit_mi_lcs_produ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.982 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.430 | NA | NA |
| d_produ_1 ~1 | -0.203 | -0.274 | -0.132 | -0.559 | -5.629 | 0.000 |
| produ_t1 ~1 | 3.461 | 3.333 | 3.589 | 4.174 | 53.095 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.132 | 0.055 | 0.209 | 1.000 | 3.348 | 0.001 |
| produ_t1 ~~ d_produ_1 | -0.054 | -0.118 | 0.011 | -0.178 | -1.630 | 0.103 |
| d_produ_1 ~~ sa06_01_t1 | -0.023 | -0.074 | 0.028 | -0.067 | -0.868 | 0.386 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.561 | 83.947 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 6678.895 | 6763.581 | 0.867 | 0.071 | 0.071 |
# parameters of interest
params_lcs_produ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_produ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.086 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.748 | NA | NA |
| d_produ_1 ~1 | -0.026 | -0.078 | 0.025 | -0.097 | -1.005 | 0.315 |
| produ_t1 ~1 | 1.612 | 1.536 | 1.689 | 4.075 | 41.302 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.074 | 0.013 | 0.136 | 1.000 | 2.369 | 0.018 |
| produ_t1 ~~ d_produ_1 | -0.049 | -0.099 | 0.001 | -0.455 | -1.923 | 0.055 |
| d_produ_1 ~~ sa06_01_t1 | -0.012 | -0.048 | 0.025 | -0.046 | -0.619 | 0.536 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.947 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8570.793 | 8655.478 | 0.89 | 0.115 | 0.153 |
# parameters of interest
params_lcs_produ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.478 | NA | NA |
| d_produ_1 ~1 | -0.201 | -0.271 | -0.131 | -0.520 | -5.611 | 0.000 |
| produ_t1 ~1 | 3.460 | 3.333 | 3.588 | 4.184 | 53.123 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.149 | 0.059 | 0.238 | 1.000 | 3.248 | 0.001 |
| produ_t1 ~~ d_produ_1 | -0.092 | -0.172 | -0.011 | -0.287 | -2.235 | 0.025 |
| d_produ_1 ~~ sa07_08_t1 | 0.099 | -0.009 | 0.208 | 0.182 | 1.798 | 0.072 |
| sa07_08_t1 ~~ sa07_08_t1 | 2.019 | 1.832 | 2.206 | 1.000 | 21.143 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 6969.792 | 7054.477 | 0.88 | 0.067 | 0.066 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.742 | NA | NA |
| d_produ_1 ~1 | -0.026 | -0.078 | 0.025 | -0.097 | -0.998 | 0.318 |
| produ_t1 ~1 | 1.612 | 1.536 | 1.689 | 4.066 | 41.284 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.074 | 0.012 | 0.135 | 1.000 | 2.360 | 0.018 |
| produ_t1 ~~ d_produ_1 | -0.049 | -0.098 | 0.001 | -0.452 | -1.912 | 0.056 |
| d_produ_1 ~~ sa07_08_t1 | 0.008 | -0.049 | 0.065 | 0.021 | 0.281 | 0.779 |
| sa07_08_t1 ~~ sa07_08_t1 | 2.019 | 1.832 | 2.206 | 1.000 | 21.143 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8327.703 | 8412.388 | 0.913 | 0.083 | 0.079 |
# parameters of interest
params_lcs_respo_curr_hyp5 <- broom::tidy(fit_mi_lcs_respo_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.397 | NA | NA |
| d_respo_1 ~1 | 0.119 | 0.055 | 0.183 | 0.458 | 3.649 | 0.000 |
| respo_t1 ~1 | 3.622 | 3.519 | 3.724 | 5.455 | 69.260 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.068 | 0.007 | 0.128 | 1.000 | 2.204 | 0.028 |
| respo_t1 ~~ d_respo_1 | -0.039 | -0.095 | 0.016 | -0.227 | -1.388 | 0.165 |
| d_respo_1 ~~ sa06_01_t1 | 0.035 | -0.012 | 0.082 | 0.145 | 1.459 | 0.145 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.561 | 83.967 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7648.902 | 7733.588 | 0.98 | 0.029 | 0.048 |
# parameters of interest
params_lcs_respo_ideal_hyp5 <- broom::tidy(fit_mi_lcs_respo_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.794 | NA | NA |
| d_respo_1 ~1 | 0.031 | -0.034 | 0.096 | 0.080 | 0.921 | 0.357 |
| respo_t1 ~1 | 4.275 | 4.175 | 4.375 | 8.429 | 83.548 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.146 | 0.036 | 0.256 | 1.000 | 2.598 | 0.009 |
| respo_t1 ~~ d_respo_1 | -0.086 | -0.158 | -0.013 | -0.442 | -2.319 | 0.020 |
| d_respo_1 ~~ sa06_01_t1 | 0.013 | -0.040 | 0.066 | 0.037 | 0.488 | 0.625 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.980 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8619.998 | 8704.683 | 0.849 | 0.114 | 0.125 |
# parameters of interest
params_lcs_respo_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.058 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.453 | NA | NA |
| d_respo_1 ~1 | 0.120 | 0.056 | 0.183 | 0.420 | 3.701 | 0.000 |
| respo_t1 ~1 | 3.621 | 3.519 | 3.724 | 5.442 | 69.342 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.081 | 0.017 | 0.146 | 1.000 | 2.470 | 0.013 |
| respo_t1 ~~ d_respo_1 | -0.064 | -0.126 | -0.002 | -0.338 | -2.027 | 0.043 |
| d_respo_1 ~~ sa07_09_t1 | -0.132 | -0.226 | -0.039 | -0.322 | -2.768 | 0.006 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.074 | 1.885 | 2.262 | 1.000 | 21.605 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7947.706 | 8032.391 | 0.98 | 0.029 | 0.046 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.792 | NA | NA |
| d_respo_1 ~1 | 0.030 | -0.035 | 0.095 | 0.079 | 0.908 | 0.364 |
| respo_t1 ~1 | 4.275 | 4.175 | 4.375 | 8.462 | 83.572 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.144 | 0.036 | 0.253 | 1.000 | 2.602 | 0.009 |
| respo_t1 ~~ d_respo_1 | -0.085 | -0.155 | -0.014 | -0.442 | -2.350 | 0.019 |
| d_respo_1 ~~ sa07_09_t1 | -0.044 | -0.125 | 0.038 | -0.080 | -1.047 | 0.295 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.074 | 1.885 | 2.262 | 1.000 | 21.610 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8706.769 | 8791.454 | 0.964 | 0.053 | 0.069 |
# parameters of interest
params_lcs_anxie_curr_hyp5 <- broom::tidy(fit_mi_lcs_anxie_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.951 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.510 | NA | NA |
| d_anxie_1 ~1 | 0.204 | 0.115 | 0.292 | 0.421 | 4.511 | 0.000 |
| anxie_t1 ~1 | 2.686 | 2.558 | 2.814 | 2.976 | 41.185 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.234 | 0.105 | 0.363 | 1.000 | 3.551 | 0.000 |
| anxie_t1 ~~ d_anxie_1 | -0.074 | -0.169 | 0.021 | -0.170 | -1.529 | 0.126 |
| d_anxie_1 ~~ sa06_01_t1 | 0.060 | -0.020 | 0.141 | 0.134 | 1.468 | 0.142 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.192 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.122 | 4.320 | 4.561 | 83.929 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8258.465 | 8343.15 | 0.906 | 0.044 | 0.048 |
# parameters of interest
params_lcs_anxie_ideal_hyp5 <- broom::tidy(fit_mi_lcs_anxie_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.864 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.771 | NA | NA |
| d_anxie_1 ~1 | -0.045 | -0.100 | 0.011 | -0.181 | -1.588 | 0.112 |
| anxie_t1 ~1 | 4.567 | 4.503 | 4.631 | 16.374 | 139.742 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.062 | -0.028 | 0.152 | 1.000 | 1.350 | 0.177 |
| anxie_t1 ~~ d_anxie_1 | -0.018 | -0.054 | 0.019 | -0.256 | -0.950 | 0.342 |
| d_anxie_1 ~~ sa06_01_t1 | 0.018 | -0.023 | 0.059 | 0.080 | 0.875 | 0.382 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.192 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.980 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 9012.64 | 9097.325 | 0.92 | 0.081 | 0.103 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.963 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.524 | NA | NA |
| d_anxie_1 ~1 | 0.202 | 0.114 | 0.290 | 0.412 | 4.500 | 0.000 |
| anxie_t1 ~1 | 2.686 | 2.558 | 2.814 | 2.986 | 41.203 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.239 | 0.106 | 0.373 | 1.000 | 3.508 | 0.000 |
| anxie_t1 ~~ d_anxie_1 | -0.088 | -0.184 | 0.008 | -0.200 | -1.793 | 0.073 |
| d_anxie_1 ~~ sa07_10_t1 | 0.011 | -0.118 | 0.140 | 0.016 | 0.169 | 0.866 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.097 | 1.913 | 2.282 | 1.000 | 22.296 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8562.512 | 8647.197 | 0.903 | 0.044 | 0.047 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.858 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.767 | NA | NA |
| d_anxie_1 ~1 | -0.046 | -0.102 | 0.010 | -0.179 | -1.603 | 0.109 |
| anxie_t1 ~1 | 4.568 | 4.503 | 4.632 | 15.921 | 139.400 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.066 | -0.029 | 0.161 | 1.000 | 1.360 | 0.174 |
| anxie_t1 ~~ d_anxie_1 | -0.018 | -0.058 | 0.022 | -0.246 | -0.892 | 0.373 |
| d_anxie_1 ~~ sa07_10_t1 | 0.014 | -0.075 | 0.103 | 0.037 | 0.305 | 0.760 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.097 | 1.913 | 2.282 | 1.000 | 22.295 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8469.485 | 8554.17 | 0.951 | 0.079 | 0.069 |
# parameters of interest
params_lcs_depre_curr_hyp5 <- broom::tidy(fit_mi_lcs_depre_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.999 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.576 | NA | NA |
| d_depre_1 ~1 | 0.198 | 0.129 | 0.267 | 0.479 | 5.645 | 0.000 |
| depre_t1 ~1 | 2.963 | 2.837 | 3.088 | 4.130 | 46.210 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.171 | 0.094 | 0.248 | 1.000 | 4.359 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.085 | -0.138 | -0.032 | -0.286 | -3.134 | 0.002 |
| d_depre_1 ~~ sa06_01_t1 | 0.028 | -0.020 | 0.075 | 0.073 | 1.152 | 0.249 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.192 | 0.000 |
| sa06_01_t1 ~1 | 4.222 | 4.123 | 4.320 | 4.561 | 84.004 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 6711.108 | 6795.793 | 0.83 | 0.081 | 0.073 |
# parameters of interest
params_lcs_depre_ideal_hyp5 <- broom::tidy(fit_mi_lcs_depre_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.062 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.810 | NA | NA |
| d_depre_1 ~1 | 0.018 | -0.019 | 0.055 | 0.081 | 0.964 | 0.335 |
| depre_t1 ~1 | 4.409 | 4.345 | 4.474 | 15.098 | 133.549 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.050 | 0.003 | 0.096 | 1.000 | 2.106 | 0.035 |
| depre_t1 ~~ d_depre_1 | -0.030 | -0.059 | -0.001 | -0.455 | -2.010 | 0.044 |
| d_depre_1 ~~ sa06_01_t1 | -0.006 | -0.034 | 0.022 | -0.028 | -0.408 | 0.684 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 84.032 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8791.845 | 8876.53 | 0.91 | 0.11 | 0.123 |
# parameters of interest
params_lcs_depre_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.023 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.603 | NA | NA |
| d_depre_1 ~1 | 0.197 | 0.129 | 0.266 | 0.466 | 5.658 | 0.000 |
| depre_t1 ~1 | 2.963 | 2.837 | 3.088 | 4.133 | 46.207 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.179 | 0.095 | 0.263 | 1.000 | 4.183 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.101 | -0.165 | -0.036 | -0.332 | -3.066 | 0.002 |
| d_depre_1 ~~ sa07_11_t1 | -0.049 | -0.158 | 0.060 | -0.078 | -0.884 | 0.377 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.217 | 2.036 | 2.399 | 1.000 | 23.933 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7033.603 | 7118.288 | 0.824 | 0.082 | 0.074 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.062 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.811 | NA | NA |
| d_depre_1 ~1 | 0.018 | -0.018 | 0.055 | 0.081 | 0.972 | 0.331 |
| depre_t1 ~1 | 4.409 | 4.345 | 4.474 | 15.086 | 133.554 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.050 | 0.003 | 0.096 | 1.000 | 2.107 | 0.035 |
| depre_t1 ~~ d_depre_1 | -0.030 | -0.059 | -0.001 | -0.456 | -2.012 | 0.044 |
| d_depre_1 ~~ sa07_11_t1 | 0.000 | -0.049 | 0.049 | -0.001 | -0.010 | 0.992 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.217 | 2.036 | 2.399 | 1.000 | 23.939 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8511.793 | 8596.478 | 0.977 | 0.052 | 0.065 |
# parameters of interest
params_lcs_volat_curr_hyp5 <- broom::tidy(fit_mi_lcs_volat_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.044 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_volat_1 ~1 | -0.143 | -0.225 | -0.062 | -0.257 | -3.456 | 0.001 |
| volat_t1 ~1 | 3.223 | 3.091 | 3.354 | 3.469 | 48.034 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.312 | 0.170 | 0.455 | 1.000 | 4.293 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.192 | -0.292 | -0.092 | -0.370 | -3.759 | 0.000 |
| d_volat_1 ~~ sa06_01_t1 | -0.071 | -0.144 | 0.001 | -0.138 | -1.924 | 0.054 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.180 | 0.000 |
| sa06_01_t1 ~1 | 4.223 | 4.125 | 4.322 | 4.561 | 84.193 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7759.602 | 7844.287 | 0.97 | 0.031 | 0.045 |
# parameters of interest
params_lcs_volat_ideal_hyp5 <- broom::tidy(fit_mi_lcs_volat_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.921 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.684 | NA | NA |
| d_volat_1 ~1 | 0.021 | -0.041 | 0.083 | 0.066 | 0.667 | 0.505 |
| volat_t1 ~1 | 1.704 | 1.623 | 1.785 | 3.950 | 41.190 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.102 | 0.024 | 0.181 | 1.000 | 2.564 | 0.010 |
| volat_t1 ~~ d_volat_1 | -0.035 | -0.087 | 0.018 | -0.251 | -1.287 | 0.198 |
| d_volat_1 ~~ sa06_01_t1 | -0.056 | -0.102 | -0.009 | -0.187 | -2.338 | 0.019 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.995 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8744.382 | 8829.067 | 0.964 | 0.065 | 0.076 |
# parameters of interest
params_lcs_volat_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.060 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.648 | NA | NA |
| d_volat_1 ~1 | -0.142 | -0.224 | -0.061 | -0.250 | -3.426 | 0.001 |
| volat_t1 ~1 | 3.223 | 3.091 | 3.354 | 3.466 | 48.022 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.323 | 0.174 | 0.472 | 1.000 | 4.243 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.209 | -0.315 | -0.103 | -0.396 | -3.876 | 0.000 |
| d_volat_1 ~~ sa07_12_t1 | -0.005 | -0.118 | 0.108 | -0.007 | -0.085 | 0.932 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.682 | 1.503 | 1.861 | 1.000 | 18.422 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7992.18 | 8076.865 | 0.955 | 0.038 | 0.046 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.911 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.666 | NA | NA |
| d_volat_1 ~1 | 0.022 | -0.040 | 0.085 | 0.071 | 0.704 | 0.482 |
| volat_t1 ~1 | 1.703 | 1.622 | 1.784 | 3.927 | 41.180 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.100 | 0.021 | 0.179 | 1.000 | 2.488 | 0.013 |
| volat_t1 ~~ d_volat_1 | -0.031 | -0.084 | 0.022 | -0.226 | -1.145 | 0.252 |
| d_volat_1 ~~ sa07_12_t1 | -0.030 | -0.101 | 0.041 | -0.072 | -0.815 | 0.415 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.682 | 1.503 | 1.861 | 1.000 | 18.422 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8292.656 | 8377.341 | 0.967 | 0.041 | 0.051 |
# parameters of interest
params_lcs_curio_curr_hyp5 <- broom::tidy(fit_mi_lcs_curio_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.162 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.693 | NA | NA |
| d_curio_1 ~1 | 0.114 | 0.041 | 0.187 | 0.311 | 3.046 | 0.002 |
| curio_t1 ~1 | 4.073 | 3.978 | 4.167 | 6.644 | 84.612 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.134 | 0.032 | 0.235 | 1.000 | 2.576 | 0.010 |
| curio_t1 ~~ d_curio_1 | -0.115 | -0.214 | -0.016 | -0.515 | -2.286 | 0.022 |
| d_curio_1 ~~ sa06_01_t1 | 0.033 | -0.021 | 0.087 | 0.097 | 1.197 | 0.231 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.193 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.122 | 4.319 | 4.560 | 83.974 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8202.72 | 8287.406 | 1 | 0 | 0.038 |
# parameters of interest
params_lcs_curio_ideal_hyp5 <- broom::tidy(fit_mi_lcs_curio_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.694 | NA | NA |
| d_curio_1 ~1 | 0.014 | -0.030 | 0.057 | 0.068 | 0.625 | 0.532 |
| curio_t1 ~1 | 4.494 | 4.426 | 4.561 | 14.600 | 130.724 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.042 | -0.006 | 0.090 | 1.000 | 1.696 | 0.090 |
| curio_t1 ~~ d_curio_1 | -0.025 | -0.051 | 0.001 | -0.399 | -1.868 | 0.062 |
| d_curio_1 ~~ sa06_01_t1 | 0.016 | -0.016 | 0.048 | 0.084 | 0.961 | 0.336 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.190 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.964 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8545.395 | 8630.08 | 0.904 | 0.073 | 0.085 |
# parameters of interest
params_lcs_curio_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.197 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.750 | NA | NA |
| d_curio_1 ~1 | 0.113 | 0.040 | 0.186 | 0.296 | 3.045 | 0.002 |
| curio_t1 ~1 | 4.073 | 3.978 | 4.167 | 6.647 | 84.739 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.147 | 0.038 | 0.257 | 1.000 | 2.630 | 0.009 |
| curio_t1 ~~ d_curio_1 | -0.130 | -0.236 | -0.025 | -0.554 | -2.425 | 0.015 |
| d_curio_1 ~~ sa07_13_t1 | -0.081 | -0.170 | 0.008 | -0.156 | -1.783 | 0.075 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.818 | 1.605 | 2.030 | 1.000 | 16.770 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8455.269 | 8539.954 | 0.978 | 0.027 | 0.046 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.052 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.707 | NA | NA |
| d_curio_1 ~1 | 0.013 | -0.030 | 0.057 | 0.063 | 0.593 | 0.553 |
| curio_t1 ~1 | 4.494 | 4.426 | 4.561 | 14.425 | 130.675 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.044 | -0.005 | 0.093 | 1.000 | 1.743 | 0.081 |
| curio_t1 ~~ d_curio_1 | -0.027 | -0.054 | 0.000 | -0.408 | -1.925 | 0.054 |
| d_curio_1 ~~ sa07_13_t1 | -0.047 | -0.105 | 0.010 | -0.168 | -1.621 | 0.105 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.817 | 1.605 | 2.030 | 1.000 | 16.761 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8351.902 | 8436.587 | 0.978 | 0.047 | 0.051 |
# parameters of interest
params_lcs_aesth_curr_hyp5 <- broom::tidy(fit_mi_lcs_aesth_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.095 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.657 | NA | NA |
| d_aesth_1 ~1 | -0.002 | -0.006 | 0.002 | -0.076 | -1.048 | 0.295 |
| aesth_t1 ~1 | 2.940 | 2.825 | 3.055 | 64.922 | 49.992 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 1.000 | 4.920 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | -0.001 | -0.001 | 0.000 | -0.439 | -4.697 | 0.000 |
| d_aesth_1 ~~ sa06_01_t1 | -0.002 | -0.005 | 0.001 | -0.075 | -1.202 | 0.229 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.990 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8261.353 | 8346.038 | 0.962 | 0.054 | 0.064 |
# parameters of interest
params_lcs_aesth_ideal_hyp5 <- broom::tidy(fit_mi_lcs_aesth_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.952 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.675 | NA | NA |
| d_aesth_1 ~1 | -0.001 | -0.005 | 0.003 | -0.045 | -0.584 | 0.559 |
| aesth_t1 ~1 | 3.208 | 3.079 | 3.338 | 92.946 | 48.630 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 1.000 | 4.164 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | -0.001 | 0.000 | -0.282 | -1.760 | 0.078 |
| d_aesth_1 ~~ sa06_01_t1 | 0.001 | -0.003 | 0.004 | 0.033 | 0.440 | 0.660 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 84.021 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8626.778 | 8711.463 | 0.971 | 0.055 | 0.066 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.090 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.648 | NA | NA |
| d_aesth_1 ~1 | -0.002 | -0.006 | 0.002 | -0.075 | -1.034 | 0.301 |
| aesth_t1 ~1 | 2.940 | 2.825 | 3.055 | 64.270 | 49.993 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 1.000 | 4.859 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | -0.001 | -0.001 | 0.000 | -0.431 | -4.567 | 0.000 |
| d_aesth_1 ~~ sa07_14_t1 | 0.000 | -0.006 | 0.005 | -0.013 | -0.188 | 0.851 |
| sa07_14_t1 ~~ sa07_14_t1 | 1.920 | 1.736 | 2.104 | 1.000 | 20.436 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8535.045 | 8619.73 | 0.987 | 0.03 | 0.041 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.948 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.670 | NA | NA |
| d_aesth_1 ~1 | -0.001 | -0.005 | 0.003 | -0.047 | -0.612 | 0.540 |
| aesth_t1 ~1 | 3.208 | 3.079 | 3.338 | 92.071 | 48.627 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 1.000 | 4.209 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | -0.273 | -1.784 | 0.074 |
| d_aesth_1 ~~ sa07_14_t1 | -0.001 | -0.005 | 0.004 | -0.019 | -0.271 | 0.786 |
| sa07_14_t1 ~~ sa07_14_t1 | 1.920 | 1.736 | 2.104 | 1.000 | 20.436 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8099.763 | 8184.449 | 0.981 | 0.04 | 0.044 |
# parameters of interest
params_lcs_imagi_curr_hyp5 <- broom::tidy(fit_mi_lcs_imagi_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.081 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.566 | NA | NA |
| d_imagi_1 ~1 | 0.102 | 0.034 | 0.169 | 0.269 | 2.955 | 0.003 |
| imagi_t1 ~1 | 3.622 | 3.523 | 3.721 | 5.017 | 71.805 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.143 | 0.054 | 0.231 | 1.000 | 3.165 | 0.002 |
| imagi_t1 ~~ d_imagi_1 | -0.109 | -0.181 | -0.037 | -0.399 | -2.948 | 0.003 |
| d_imagi_1 ~~ sa06_01_t1 | 0.029 | -0.027 | 0.084 | 0.082 | 1.013 | 0.311 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.022 | 1.000 | 10.194 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.560 | 83.985 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7601.828 | 7686.513 | 0.916 | 0.053 | 0.06 |
# parameters of interest
params_lcs_imagi_ideal_hyp5 <- broom::tidy(fit_mi_lcs_imagi_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.871 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.543 | NA | NA |
| d_imagi_1 ~1 | -0.044 | -0.092 | 0.004 | -0.214 | -1.790 | 0.074 |
| imagi_t1 ~1 | 4.603 | 4.544 | 4.663 | 13.995 | 151.369 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.042 | 0.000 | 0.084 | 1.000 | 1.968 | 0.049 |
| imagi_t1 ~~ d_imagi_1 | -0.004 | -0.034 | 0.026 | -0.057 | -0.250 | 0.803 |
| d_imagi_1 ~~ sa06_01_t1 | -0.015 | -0.053 | 0.024 | -0.076 | -0.732 | 0.464 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.857 | 0.692 | 1.021 | 1.000 | 10.191 | 0.000 |
| sa06_01_t1 ~1 | 4.221 | 4.123 | 4.320 | 4.561 | 83.935 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 8400.785 | 8485.47 | 0.928 | 0.08 | 0.105 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.094 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.582 | NA | NA |
| d_imagi_1 ~1 | 0.101 | 0.033 | 0.168 | 0.263 | 2.932 | 0.003 |
| imagi_t1 ~1 | 3.622 | 3.523 | 3.721 | 5.020 | 71.837 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.147 | 0.058 | 0.236 | 1.000 | 3.232 | 0.001 |
| imagi_t1 ~~ d_imagi_1 | -0.116 | -0.189 | -0.044 | -0.420 | -3.152 | 0.002 |
| d_imagi_1 ~~ sa07_15_t1 | -0.046 | -0.137 | 0.044 | -0.084 | -1.001 | 0.317 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.082 | 1.901 | 2.263 | 1.000 | 22.531 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 22 | 7903.232 | 7987.917 | 0.928 | 0.048 | 0.058 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.874 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.550 | NA | NA |
| d_imagi_1 ~1 | -0.044 | -0.092 | 0.004 | -0.211 | -1.783 | 0.075 |
| imagi_t1 ~1 | 4.603 | 4.544 | 4.663 | 13.998 | 151.411 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.043 | 0.000 | 0.085 | 1.000 | 1.979 | 0.048 |
| imagi_t1 ~~ d_imagi_1 | -0.005 | -0.035 | 0.025 | -0.068 | -0.302 | 0.763 |
| d_imagi_1 ~~ sa07_15_t1 | 0.008 | -0.052 | 0.068 | 0.027 | 0.262 | 0.793 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.082 | 1.900 | 2.263 | 1.000 | 22.520 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp5 <- bind_rows(
#traits
params_lcs_extra_curr_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_ideal_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_curr_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_ideal_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_curr_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_ideal_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_curr_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_ideal_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_curr_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_ideal_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_curr_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_ideal_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_curr_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_socia_ideal_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_asser_curr_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_ideal_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_curr_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_asser_ideal_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_energ_curr_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_ideal_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_curr_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_energ_ideal_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_compa_curr_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_ideal_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_curr_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_compa_ideal_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_respe_curr_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_ideal_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_curr_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_respe_ideal_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_trust_curr_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_ideal_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_curr_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_trust_ideal_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_organ_curr_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_ideal_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_curr_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_organ_ideal_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_produ_curr_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_ideal_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_curr_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_produ_ideal_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_respo_curr_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_ideal_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_curr_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_respo_ideal_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_anxie_curr_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_ideal_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_curr_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_anxie_ideal_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_depre_curr_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_ideal_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_curr_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_depre_ideal_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_volat_curr_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_ideal_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_curr_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_volat_ideal_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_curio_curr_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_ideal_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_curr_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_curio_ideal_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_aesth_curr_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_ideal_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_curr_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_aesth_ideal_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_imagi_curr_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_ideal_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_curr_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
params_lcs_imagi_ideal_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[1:20, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extraversion | current | general | 0.062 | 0.193 | 3.212 | 0.001 |
| extraversion | ideal | general | 0.024 | 0.076 | 1.114 | 0.265 |
| extraversion | current | specific | 0.017 | 0.052 | 0.496 | 0.620 |
| extraversion | ideal | specific | -0.031 | -0.097 | -0.905 | 0.365 |
| agreeableness | current | general | 0.027 | 0.145 | 1.520 | 0.129 |
| agreeableness | ideal | general | 0.024 | 0.084 | 1.219 | 0.223 |
| agreeableness | current | specific | 0.005 | 0.035 | 0.322 | 0.748 |
| agreeableness | ideal | specific | 0.008 | 0.035 | 0.383 | 0.701 |
| conscientiousness | current | general | 0.020 | 0.060 | 1.025 | 0.305 |
| conscientiousness | ideal | general | -0.006 | -0.020 | -0.344 | 0.731 |
| conscientiousness | current | specific | -0.108 | -0.256 | -2.610 | 0.009 |
| conscientiousness | ideal | specific | -0.032 | -0.083 | -1.310 | 0.190 |
| neuroticism | current | general | -0.063 | -0.157 | -2.306 | 0.021 |
| neuroticism | ideal | general | -0.013 | -0.043 | -0.728 | 0.467 |
| neuroticism | current | specific | 0.020 | 0.053 | 0.650 | 0.516 |
| neuroticism | ideal | specific | 0.036 | 0.140 | 2.319 | 0.020 |
| openness | current | general | 0.025 | 0.090 | 1.354 | 0.176 |
| openness | ideal | general | 0.001 | 0.007 | 0.088 | 0.930 |
| openness | current | specific | -0.014 | -0.059 | -0.747 | 0.455 |
| openness | ideal | specific | -0.003 | -0.016 | -0.193 | 0.847 |
Results summary across the Big Five facets: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[21:80, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| sociability | current | general | 0.089 | 0.268 | 3.243 | 0.001 |
| sociability | ideal | general | -0.013 | -0.060 | -0.767 | 0.443 |
| sociability | current | specific | 0.002 | 0.004 | 0.039 | 0.969 |
| sociability | ideal | specific | -0.009 | -0.027 | -0.262 | 0.793 |
| assertiveness | current | general | 0.056 | 0.181 | 2.425 | 0.015 |
| assertiveness | ideal | general | 0.015 | 0.089 | 1.023 | 0.306 |
| assertiveness | current | specific | -0.010 | -0.022 | -0.253 | 0.800 |
| assertiveness | ideal | specific | -0.021 | -0.084 | -0.893 | 0.372 |
| energy | current | general | -0.019 | -0.068 | -0.985 | 0.325 |
| energy | ideal | general | -0.028 | -0.147 | -1.512 | 0.130 |
| energy | current | specific | 0.054 | 0.129 | 1.373 | 0.170 |
| energy | ideal | specific | 0.042 | 0.147 | 1.420 | 0.156 |
| compassion | current | general | 0.060 | 0.142 | 1.452 | 0.147 |
| compassion | ideal | general | 0.012 | 0.041 | 0.402 | 0.688 |
| compassion | current | specific | -0.027 | -0.046 | -0.574 | 0.566 |
| compassion | ideal | specific | -0.088 | -0.214 | -1.988 | 0.047 |
| respectfulness | current | general | 0.012 | 0.049 | 0.539 | 0.590 |
| respectfulness | ideal | general | 0.011 | 0.054 | 0.624 | 0.533 |
| respectfulness | current | specific | 0.013 | 0.038 | 0.388 | 0.698 |
| respectfulness | ideal | specific | -0.009 | -0.031 | -0.315 | 0.753 |
| trust | current | general | -0.019 | -0.067 | -0.717 | 0.473 |
| trust | ideal | general | -0.045 | -0.174 | -1.867 | 0.062 |
| trust | current | specific | 0.045 | 0.109 | 1.213 | 0.225 |
| trust | ideal | specific | 0.010 | 0.026 | 0.281 | 0.779 |
| organization | current | general | -0.004 | -0.010 | -0.133 | 0.894 |
| organization | ideal | general | 0.007 | 0.041 | 0.528 | 0.598 |
| organization | current | specific | 0.164 | 0.236 | 2.380 | 0.017 |
| organization | ideal | specific | 0.015 | 0.055 | 0.704 | 0.481 |
| productiveness | current | general | -0.023 | -0.068 | -0.868 | 0.386 |
| productiveness | ideal | general | -0.012 | -0.051 | -0.619 | 0.536 |
| productiveness | current | specific | 0.099 | 0.190 | 1.798 | 0.072 |
| productiveness | ideal | specific | 0.008 | 0.024 | 0.281 | 0.779 |
| responsibility | current | general | 0.035 | 0.149 | 1.459 | 0.145 |
| responsibility | ideal | general | 0.013 | 0.042 | 0.488 | 0.625 |
| responsibility | current | specific | -0.132 | -0.342 | -2.768 | 0.006 |
| responsibility | ideal | specific | -0.044 | -0.089 | -1.047 | 0.295 |
| anxiety | current | general | 0.060 | 0.136 | 1.468 | 0.142 |
| anxiety | ideal | general | 0.018 | 0.082 | 0.875 | 0.382 |
| anxiety | current | specific | 0.011 | 0.016 | 0.169 | 0.866 |
| anxiety | ideal | specific | 0.014 | 0.038 | 0.305 | 0.760 |
| depression | current | general | 0.028 | 0.076 | 1.152 | 0.249 |
| depression | ideal | general | -0.006 | -0.032 | -0.408 | 0.684 |
| depression | current | specific | -0.049 | -0.083 | -0.884 | 0.377 |
| depression | ideal | specific | 0.000 | -0.001 | -0.010 | 0.992 |
| volatility | current | general | -0.071 | -0.148 | -1.923 | 0.054 |
| volatility | ideal | general | -0.056 | -0.194 | -2.338 | 0.019 |
| volatility | current | specific | -0.005 | -0.007 | -0.085 | 0.932 |
| volatility | ideal | specific | -0.030 | -0.074 | -0.815 | 0.415 |
| curiosity | current | general | 0.033 | 0.114 | 1.197 | 0.231 |
| curiosity | ideal | general | 0.016 | 0.092 | 0.961 | 0.336 |
| curiosity | current | specific | -0.081 | -0.187 | -1.783 | 0.075 |
| curiosity | ideal | specific | -0.047 | -0.184 | -1.621 | 0.105 |
| aesthetic | current | general | -0.002 | -0.084 | -1.202 | 0.229 |
| aesthetic | ideal | general | 0.001 | 0.034 | 0.440 | 0.660 |
| aesthetic | current | specific | 0.000 | -0.015 | -0.188 | 0.851 |
| aesthetic | ideal | specific | -0.001 | -0.020 | -0.271 | 0.786 |
| imagination | current | general | 0.029 | 0.089 | 1.013 | 0.311 |
| imagination | ideal | general | -0.015 | -0.076 | -0.732 | 0.464 |
| imagination | current | specific | -0.046 | -0.092 | -1.001 | 0.317 |
| imagination | ideal | specific | 0.008 | 0.027 | 0.262 | 0.793 |
Prepare data frame for plotting:
df_table_hyp5_plot <- bind_rows(
#traits
params_lcs_extra_curr_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_ideal_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_curr_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_ideal_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_curr_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_ideal_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_curr_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_ideal_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_curr_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_ideal_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_curr_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_ideal_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_curr_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_socia_ideal_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_asser_curr_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_ideal_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_curr_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_asser_ideal_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_energ_curr_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_ideal_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_curr_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_energ_ideal_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_compa_curr_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_ideal_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_curr_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_compa_ideal_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_respe_curr_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_ideal_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_curr_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_respe_ideal_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_trust_curr_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_ideal_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_curr_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_trust_ideal_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_organ_curr_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_ideal_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_curr_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_organ_ideal_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_produ_curr_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_ideal_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_curr_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_produ_ideal_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_respo_curr_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_ideal_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_curr_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_respo_ideal_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_anxie_curr_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_ideal_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_curr_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_anxie_ideal_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_depre_curr_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_ideal_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_curr_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_depre_ideal_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_volat_curr_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_ideal_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_curr_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_volat_ideal_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_curio_curr_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_ideal_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_curr_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_curio_ideal_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_aesth_curr_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_ideal_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_curr_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_aesth_ideal_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_imagi_curr_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_ideal_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_curr_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
params_lcs_imagi_ideal_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp5_plot <- df_table_hyp5_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor(ref, levels = c("current", "ideal"), labels = c("current", "ideal"))) %>%
mutate(goal = factor(goal, levels = c("general", "specific"), labels = c("general", "specific"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp5_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + goal, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Desire to change and frequency of self-improvement behaviors measured at the follow-up assessment will be positively related to change in current-self ratings in the self-improvement group.
To test this hypothesis, we will estimate the mean-level difference in current trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to change on a given big five domain or facet. The second will indicate their frequency of self-improvement behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sb_mod <- df_sbsa %>%
filter(rando=="Self-Improvement") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07", # facet-specific change goals
"sb04"))) %>% # frequency self-improvement behaviors
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sb07", "sb04")))) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2,
sb04_01_t1, sb04_02_t1, sb04_03_t1)) # frequency of self-improvement behaviors measured at T2
# colnames(df_sbsa_wide_pers_sb_mod)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sb_mod <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Improvement") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sb_mod)Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific change goals
trait_template_mod_goal <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~~ d_trait_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
trait_t1 ~ goals # This estimates the moderation effect on personality at T1
d_trait_1 ~ goals # This estimates the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
trait_facets_nrs <- list(a1 = c(1:3), b2 = c(4:6), c3 = c(7:9), d4 = c(10:12), e5 = c(13:15)) # matching facet nrs to traits
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
mod_names = paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2],
"ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), trait_model_fit))
}
# 2nd, for frequency of self-improvement behavior
trait_template_mod_frequ <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~~ d_trait_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
trait_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_trait_1 ~ frequ # This estimates the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), trait_model_fit))
} Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 7065.443 | 7167.053 | 0.98 | 0.051 | 0.042 |
# parameters of interest
params_lcs_extra_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.004 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.612 | NA | NA |
| d_extra_1 ~1 | 0.134 | 0.086 | 0.182 | 0.380 | 5.437 | 0.000 |
| extra_t1 ~1 | 2.755 | 2.684 | 2.827 | 4.769 | 75.569 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.115 | 0.071 | 0.159 | 0.930 | 5.137 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.032 | -0.062 | -0.003 | -0.202 | -2.173 | 0.030 |
| extra_t1 ~ goals | -0.460 | -0.618 | -0.301 | -0.571 | -5.691 | 0.000 |
| d_extra_1 ~ goals | 0.129 | 0.019 | 0.240 | 0.264 | 2.296 | 0.022 |
| goals ~~ goals | 0.516 | 0.250 | 0.781 | 1.000 | 3.803 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6510.957 | 6612.567 | 0.985 | 0.046 | 0.051 |
# parameters of interest
params_lcs_extra_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_extra_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.609 | NA | NA |
| d_extra_1 ~1 | 0.132 | 0.085 | 0.180 | 0.381 | 5.426 | 0.000 |
| extra_t1 ~1 | 2.756 | 2.685 | 2.827 | 4.811 | 75.587 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.120 | 0.073 | 0.168 | 0.994 | 4.961 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.065 | -0.099 | -0.031 | -0.334 | -3.751 | 0.000 |
| extra_t1 ~ frequ | 0.175 | 0.040 | 0.310 | 0.213 | 2.538 | 0.011 |
| d_extra_1 ~ frequ | 0.040 | -0.046 | 0.125 | 0.080 | 0.913 | 0.361 |
| frequ ~~ frequ | 0.488 | 0.337 | 0.639 | 1.000 | 6.351 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6710.102 | 6811.712 | 0.981 | 0.045 | 0.044 |
# parameters of interest
params_lcs_agree_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.064 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_agree_1 ~1 | 0.075 | 0.038 | 0.112 | 0.355 | 3.983 | 0.000 |
| agree_t1 ~1 | 3.357 | 3.296 | 3.418 | 8.041 | 107.891 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.043 | 0.021 | 0.065 | 0.961 | 3.870 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.027 | -0.043 | -0.011 | -0.329 | -3.352 | 0.001 |
| agree_t1 ~ goals | -0.157 | -0.243 | -0.072 | -0.319 | -3.617 | 0.000 |
| d_agree_1 ~ goals | 0.050 | -0.002 | 0.101 | 0.198 | 1.902 | 0.057 |
| goals ~~ goals | 0.715 | 0.394 | 1.037 | 1.000 | 4.361 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 5969.657 | 6071.267 | 0.991 | 0.031 | 0.04 |
# parameters of interest
params_lcs_agree_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_agree_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.060 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_agree_1 ~1 | 0.078 | 0.039 | 0.116 | 0.358 | 3.979 | 0.000 |
| agree_t1 ~1 | 3.356 | 3.295 | 3.417 | 7.890 | 107.487 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.046 | 0.023 | 0.069 | 0.973 | 3.923 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.036 | -0.055 | -0.018 | -0.408 | -3.857 | 0.000 |
| agree_t1 ~ frequ | 0.115 | 0.030 | 0.201 | 0.194 | 2.643 | 0.008 |
| d_agree_1 ~ frequ | 0.049 | -0.011 | 0.110 | 0.164 | 1.607 | 0.108 |
| frequ ~~ frequ | 0.514 | 0.363 | 0.666 | 1.000 | 6.639 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6788.814 | 6890.424 | 0.981 | 0.063 | 0.035 |
# parameters of interest
params_lcs_consc_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.506 | NA | NA |
| d_consc_1 ~1 | 0.123 | 0.074 | 0.172 | 0.342 | 4.963 | 0.000 |
| consc_t1 ~1 | 3.552 | 3.469 | 3.636 | 4.826 | 83.372 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.124 | 0.081 | 0.167 | 0.962 | 5.686 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.053 | -0.089 | -0.016 | -0.254 | -2.848 | 0.004 |
| consc_t1 ~ goals | -0.366 | -0.434 | -0.299 | -0.600 | -10.613 | 0.000 |
| d_consc_1 ~ goals | 0.058 | 0.014 | 0.102 | 0.195 | 2.593 | 0.010 |
| goals ~~ goals | 1.454 | 1.152 | 1.756 | 1.000 | 9.441 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6208.3 | 6309.91 | 0.994 | 0.034 | 0.032 |
# parameters of interest
params_lcs_consc_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_consc_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.507 | NA | NA |
| d_consc_1 ~1 | 0.124 | 0.075 | 0.173 | 0.342 | 4.951 | 0.000 |
| consc_t1 ~1 | 3.551 | 3.468 | 3.635 | 4.795 | 83.278 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.129 | 0.085 | 0.172 | 0.983 | 5.781 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.089 | -0.131 | -0.048 | -0.340 | -4.202 | 0.000 |
| consc_t1 ~ frequ | 0.139 | -0.004 | 0.282 | 0.133 | 1.907 | 0.056 |
| d_consc_1 ~ frequ | 0.067 | -0.008 | 0.141 | 0.130 | 1.754 | 0.079 |
| frequ ~~ frequ | 0.503 | 0.352 | 0.654 | 1.000 | 6.516 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 7355.95 | 7457.561 | 0.987 | 0.053 | 0.03 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_neuro_1 ~1 | -0.211 | -0.273 | -0.149 | -0.425 | -6.688 | 0.000 |
| neuro_t1 ~1 | 3.492 | 3.400 | 3.584 | 4.295 | 74.420 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.245 | 0.171 | 0.320 | 0.988 | 6.445 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.113 | -0.166 | -0.060 | -0.367 | -4.201 | 0.000 |
| neuro_t1 ~ goals | 0.452 | 0.364 | 0.541 | 0.643 | 10.023 | 0.000 |
| d_neuro_1 ~ goals | -0.047 | -0.111 | 0.017 | -0.108 | -1.429 | 0.153 |
| goals ~~ goals | 1.335 | 1.020 | 1.650 | 1.000 | 8.309 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6688.857 | 6790.468 | 0.988 | 0.049 | 0.042 |
# parameters of interest
params_lcs_neuro_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_neuro_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.030 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.630 | NA | NA |
| d_neuro_1 ~1 | -0.211 | -0.272 | -0.149 | -0.429 | -6.725 | 0.000 |
| neuro_t1 ~1 | 3.491 | 3.399 | 3.582 | 4.346 | 74.573 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.238 | 0.166 | 0.311 | 0.987 | 6.470 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.137 | -0.192 | -0.081 | -0.349 | -4.834 | 0.000 |
| neuro_t1 ~ frequ | 0.063 | -0.096 | 0.222 | 0.056 | 0.778 | 0.437 |
| d_neuro_1 ~ frequ | -0.080 | -0.190 | 0.031 | -0.115 | -1.417 | 0.157 |
| frequ ~~ frequ | 0.502 | 0.349 | 0.655 | 1.000 | 6.433 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6817.085 | 6918.695 | 0.972 | 0.064 | 0.053 |
# parameters of interest
params_lcs_openn_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.912 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.411 | NA | NA |
| d_openn_1 ~1 | 0.058 | 0.022 | 0.093 | 0.294 | 3.175 | 0.001 |
| openn_t1 ~1 | 3.435 | 3.367 | 3.503 | 7.897 | 98.482 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.035 | 0.015 | 0.055 | 0.916 | 3.398 | 0.001 |
| openn_t1 ~~ d_openn_1 | 0.007 | -0.008 | 0.022 | 0.087 | 0.882 | 0.378 |
| openn_t1 ~ goals | -0.202 | -0.315 | -0.090 | -0.279 | -3.517 | 0.000 |
| d_openn_1 ~ goals | 0.095 | 0.026 | 0.163 | 0.290 | 2.712 | 0.007 |
| goals ~~ goals | 0.359 | 0.194 | 0.525 | 1.000 | 4.251 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 26 | 6280.569 | 6382.179 | 0.992 | 0.034 | 0.032 |
# parameters of interest
params_lcs_openn_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_openn_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.912 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.410 | NA | NA |
| d_openn_1 ~1 | 0.056 | 0.021 | 0.092 | 0.291 | 3.131 | 0.002 |
| openn_t1 ~1 | 3.435 | 3.367 | 3.504 | 7.948 | 98.534 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.035 | 0.015 | 0.055 | 0.936 | 3.466 | 0.001 |
| openn_t1 ~~ d_openn_1 | -0.005 | -0.020 | 0.010 | -0.068 | -0.689 | 0.491 |
| openn_t1 ~ frequ | 0.152 | 0.065 | 0.240 | 0.247 | 3.404 | 0.001 |
| d_openn_1 ~ frequ | 0.070 | 0.006 | 0.134 | 0.254 | 2.148 | 0.032 |
| frequ ~~ frequ | 0.492 | 0.344 | 0.641 | 1.000 | 6.497 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific change goal
facet_template_mod_goal <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1
d_facet_1 ~ ind_goal # This estimates the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
mod_name = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), facet_model_fit))
}
# 2nd, for frequency of self-improvement behavior
facet_template_mod_frequ <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
facet_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_facet_1 ~ frequ # This estimates the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), facet_model_fit))
} Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8762.653 | 8852.539 | 0.953 | 0.074 | 0.044 |
# parameters of interest
params_lcs_socia_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", "d_socia_1 ~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.028 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.646 | NA | NA |
| d_socia_1 ~1 | -0.066 | -0.301 | 0.168 | -0.124 | -0.555 | 0.579 |
| socia_t1 ~1 | 3.959 | 3.602 | 4.315 | 4.665 | 21.781 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.277 | 0.166 | 0.388 | 0.975 | 4.903 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.129 | -0.201 | -0.057 | -0.325 | -3.534 | 0.000 |
| d_socia_1 ~ sb07_01_t1 | 0.070 | 0.006 | 0.134 | 0.158 | 2.137 | 0.033 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.451 | 1.261 | 1.641 | 1.000 | 14.953 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10451.08 | 10568.32 | 0.959 | 0.06 | 0.049 |
# parameters of interest
params_lcs_socia_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_socia_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.030 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.645 | NA | NA |
| d_socia_1 ~1 | 0.179 | 0.093 | 0.265 | 0.340 | 4.084 | 0.000 |
| socia_t1 ~1 | 2.806 | 2.682 | 2.929 | 3.349 | 44.424 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.276 | 0.163 | 0.389 | 1.000 | 4.775 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.156 | -0.237 | -0.075 | -0.362 | -3.784 | 0.000 |
| socia_t1 ~ frequ | 0.231 | 0.039 | 0.423 | 0.195 | 2.354 | 0.019 |
| d_socia_1 ~ frequ | -0.012 | -0.138 | 0.115 | -0.016 | -0.180 | 0.857 |
| frequ ~~ frequ | 0.503 | 0.353 | 0.653 | 1.000 | 6.576 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9099.907 | 9189.793 | 0.976 | 0.043 | 0.045 |
# parameters of interest
params_lcs_asser_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", "d_asser_1 ~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.424 | NA | NA |
| d_asser_1 ~1 | -0.075 | -0.237 | 0.088 | -0.296 | -0.902 | 0.367 |
| asser_t1 ~1 | 3.498 | 3.228 | 3.768 | 5.926 | 25.404 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.061 | 0.008 | 0.115 | 0.969 | 2.240 | 0.025 |
| asser_t1 ~~ d_asser_1 | -0.023 | -0.062 | 0.016 | -0.163 | -1.156 | 0.248 |
| d_asser_1 ~ sb07_02_t1 | 0.038 | -0.011 | 0.087 | 0.177 | 1.539 | 0.124 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.346 | 1.177 | 1.515 | 1.000 | 15.654 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10760.98 | 10878.22 | 0.974 | 0.042 | 0.047 |
# parameters of interest
params_lcs_asser_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_asser_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.992 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.425 | NA | NA |
| d_asser_1 ~1 | 0.056 | 0.003 | 0.109 | 0.228 | 2.067 | 0.039 |
| asser_t1 ~1 | 3.059 | 2.958 | 3.161 | 5.355 | 59.063 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.058 | 0.008 | 0.108 | 0.973 | 2.294 | 0.022 |
| asser_t1 ~~ d_asser_1 | -0.031 | -0.069 | 0.006 | -0.232 | -1.626 | 0.104 |
| asser_t1 ~ frequ | 0.147 | 0.017 | 0.277 | 0.182 | 2.216 | 0.027 |
| d_asser_1 ~ frequ | 0.057 | -0.033 | 0.146 | 0.163 | 1.237 | 0.216 |
| frequ ~~ frequ | 0.498 | 0.349 | 0.648 | 1.000 | 6.527 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9098.864 | 9188.75 | 0.952 | 0.063 | 0.045 |
# parameters of interest
params_lcs_energ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", "d_energ_1 ~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.557 | NA | NA |
| d_energ_1 ~1 | -0.011 | -0.107 | 0.084 | -0.054 | -0.229 | 0.819 |
| energ_t1 ~1 | 2.239 | 2.038 | 2.440 | 6.039 | 21.810 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.042 | 0.007 | 0.077 | 0.986 | 2.351 | 0.019 |
| energ_t1 ~~ d_energ_1 | -0.017 | -0.036 | 0.001 | -0.251 | -1.813 | 0.070 |
| d_energ_1 ~ sb07_03_t1 | -0.020 | -0.046 | 0.006 | -0.118 | -1.497 | 0.134 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.526 | 1.316 | 1.735 | 1.000 | 14.284 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10741.64 | 10858.88 | 0.958 | 0.053 | 0.053 |
# parameters of interest
params_lcs_energ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_energ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.551 | NA | NA |
| d_energ_1 ~1 | -0.091 | -0.136 | -0.046 | -0.418 | -3.960 | 0.000 |
| energ_t1 ~1 | 2.709 | 2.609 | 2.808 | 6.913 | 53.412 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.047 | 0.010 | 0.084 | 0.993 | 2.507 | 0.012 |
| energ_t1 ~~ d_energ_1 | -0.024 | -0.047 | -0.001 | -0.297 | -2.088 | 0.037 |
| energ_t1 ~ frequ | -0.144 | -0.252 | -0.036 | -0.256 | -2.611 | 0.009 |
| d_energ_1 ~ frequ | -0.026 | -0.094 | 0.043 | -0.082 | -0.729 | 0.466 |
| frequ ~~ frequ | 0.483 | 0.330 | 0.637 | 1.000 | 6.190 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8930.983 | 9020.869 | 0.996 | 0.014 | 0.034 |
# parameters of interest
params_lcs_compa_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", "d_compa_1 ~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.139 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.403 | NA | NA |
| d_compa_1 ~1 | 0.005 | -0.126 | 0.135 | 0.018 | 0.069 | 0.945 |
| compa_t1 ~1 | 4.465 | 4.287 | 4.643 | 6.300 | 49.154 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.060 | -0.015 | 0.136 | 0.957 | 1.558 | 0.119 |
| compa_t1 ~~ d_compa_1 | -0.080 | -0.153 | -0.008 | -0.475 | -2.176 | 0.030 |
| d_compa_1 ~ sb07_04_t1 | 0.037 | -0.010 | 0.085 | 0.207 | 1.541 | 0.123 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.938 | 1.757 | 2.119 | 1.000 | 21.039 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10452.25 | 10569.49 | 0.988 | 0.026 | 0.043 |
# parameters of interest
params_lcs_compa_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_compa_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.138 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.401 | NA | NA |
| d_compa_1 ~1 | 0.103 | 0.037 | 0.170 | 0.416 | 3.063 | 0.002 |
| compa_t1 ~1 | 4.149 | 4.059 | 4.239 | 5.872 | 89.984 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.058 | -0.017 | 0.133 | 0.939 | 1.524 | 0.127 |
| compa_t1 ~~ d_compa_1 | -0.097 | -0.171 | -0.023 | -0.582 | -2.576 | 0.010 |
| compa_t1 ~ frequ | 0.206 | 0.065 | 0.347 | 0.210 | 2.859 | 0.004 |
| d_compa_1 ~ frequ | 0.085 | -0.017 | 0.188 | 0.247 | 1.635 | 0.102 |
| frequ ~~ frequ | 0.519 | 0.367 | 0.670 | 1.000 | 6.723 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8127.341 | 8217.227 | 0.944 | 0.068 | 0.056 |
# parameters of interest
params_lcs_respe_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", "d_respe_1 ~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.128 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.632 | NA | NA |
| d_respe_1 ~1 | -0.106 | -0.207 | -0.006 | -0.319 | -2.076 | 0.038 |
| respe_t1 ~1 | 4.825 | 4.701 | 4.948 | 8.118 | 76.584 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.100 | 0.050 | 0.150 | 0.906 | 3.918 | 0.000 |
| respe_t1 ~~ d_respe_1 | -0.068 | -0.118 | -0.017 | -0.396 | -2.617 | 0.009 |
| d_respe_1 ~ sb07_05_t1 | 0.076 | 0.029 | 0.123 | 0.307 | 3.178 | 0.001 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.811 | 1.606 | 2.015 | 1.000 | 17.355 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 9723.922 | 9841.164 | 0.952 | 0.057 | 0.053 |
# parameters of interest
params_lcs_respe_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respe_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.127 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_respe_1 ~1 | 0.065 | 0.011 | 0.120 | 0.199 | 2.338 | 0.019 |
| respe_t1 ~1 | 4.398 | 4.321 | 4.475 | 7.542 | 111.826 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.106 | 0.053 | 0.160 | 0.998 | 3.897 | 0.000 |
| respe_t1 ~~ d_respe_1 | -0.091 | -0.149 | -0.032 | -0.482 | -3.046 | 0.002 |
| respe_t1 ~ frequ | 0.122 | 0.000 | 0.243 | 0.149 | 1.959 | 0.050 |
| d_respe_1 ~ frequ | 0.020 | -0.069 | 0.109 | 0.043 | 0.434 | 0.664 |
| frequ ~~ frequ | 0.512 | 0.360 | 0.663 | 1.000 | 6.602 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9284.387 | 9374.273 | 0.955 | 0.056 | 0.044 |
# parameters of interest
params_lcs_trust_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", "d_trust_1 ~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.558 | NA | NA |
| d_trust_1 ~1 | -0.076 | -0.211 | 0.058 | -0.234 | -1.111 | 0.267 |
| trust_t1 ~1 | 2.772 | 2.578 | 2.967 | 4.630 | 27.902 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.106 | 0.014 | 0.198 | 0.997 | 2.262 | 0.024 |
| trust_t1 ~~ d_trust_1 | -0.061 | -0.122 | 0.000 | -0.314 | -1.968 | 0.049 |
| d_trust_1 ~ sb07_06_t1 | -0.013 | -0.061 | 0.036 | -0.052 | -0.506 | 0.613 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.807 | 1.641 | 1.972 | 1.000 | 21.378 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10829.68 | 10946.93 | 0.973 | 0.041 | 0.04 |
# parameters of interest
params_lcs_trust_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_trust_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.555 | NA | NA |
| d_trust_1 ~1 | -0.111 | -0.182 | -0.040 | -0.345 | -3.071 | 0.002 |
| trust_t1 ~1 | 2.876 | 2.764 | 2.988 | 4.824 | 50.239 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.102 | 0.013 | 0.191 | 0.984 | 2.246 | 0.025 |
| trust_t1 ~~ d_trust_1 | -0.065 | -0.127 | -0.002 | -0.342 | -2.032 | 0.042 |
| trust_t1 ~ frequ | -0.122 | -0.248 | 0.004 | -0.146 | -1.895 | 0.058 |
| d_trust_1 ~ frequ | -0.058 | -0.164 | 0.048 | -0.128 | -1.072 | 0.284 |
| frequ ~~ frequ | 0.509 | 0.358 | 0.660 | 1.000 | 6.615 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8783.838 | 8873.724 | 0.945 | 0.088 | 0.045 |
# parameters of interest
params_lcs_organ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", "d_organ_1 ~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.078 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.539 | NA | NA |
| d_organ_1 ~1 | 0.147 | -0.048 | 0.342 | 0.274 | 1.476 | 0.140 |
| organ_t1 ~1 | 1.421 | 1.124 | 1.719 | 1.324 | 9.366 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.271 | 0.150 | 0.392 | 0.943 | 4.402 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.143 | -0.231 | -0.054 | -0.316 | -3.156 | 0.002 |
| d_organ_1 ~ sb07_07_t1 | -0.087 | -0.146 | -0.028 | -0.239 | -2.894 | 0.004 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.176 | 1.971 | 2.382 | 1.000 | 20.723 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10380.81 | 10498.06 | 0.983 | 0.042 | 0.035 |
# parameters of interest
params_lcs_organ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_organ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.077 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.542 | NA | NA |
| d_organ_1 ~1 | -0.138 | -0.216 | -0.059 | -0.267 | -3.444 | 0.001 |
| organ_t1 ~1 | 2.864 | 2.729 | 3.000 | 2.801 | 41.417 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.264 | 0.149 | 0.380 | 0.997 | 4.480 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.211 | -0.309 | -0.112 | -0.408 | -4.198 | 0.000 |
| organ_t1 ~ frequ | -0.276 | -0.470 | -0.083 | -0.193 | -2.800 | 0.005 |
| d_organ_1 ~ frequ | -0.041 | -0.163 | 0.080 | -0.057 | -0.669 | 0.504 |
| frequ ~~ frequ | 0.511 | 0.359 | 0.663 | 1.000 | 6.593 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8663.677 | 8753.563 | 0.978 | 0.048 | 0.036 |
# parameters of interest
params_lcs_produ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", "d_produ_1 ~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.050 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.509 | NA | NA |
| d_produ_1 ~1 | 0.064 | -0.131 | 0.260 | 0.170 | 0.643 | 0.520 |
| produ_t1 ~1 | 2.228 | 1.897 | 2.559 | 2.859 | 13.195 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.138 | 0.069 | 0.206 | 0.964 | 3.916 | 0.000 |
| produ_t1 ~~ d_produ_1 | -0.073 | -0.129 | -0.016 | -0.287 | -2.526 | 0.012 |
| d_produ_1 ~ sb07_08_t1 | -0.056 | -0.107 | -0.006 | -0.189 | -2.195 | 0.028 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.606 | 1.377 | 1.835 | 1.000 | 13.728 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10321.73 | 10438.97 | 0.976 | 0.044 | 0.043 |
# parameters of interest
params_lcs_produ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_produ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.049 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.513 | NA | NA |
| d_produ_1 ~1 | -0.152 | -0.219 | -0.085 | -0.406 | -4.426 | 0.000 |
| produ_t1 ~1 | 3.377 | 3.256 | 3.497 | 4.416 | 54.910 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.136 | 0.069 | 0.204 | 0.975 | 3.957 | 0.000 |
| produ_t1 ~~ d_produ_1 | -0.102 | -0.166 | -0.038 | -0.364 | -3.126 | 0.002 |
| produ_t1 ~ frequ | -0.133 | -0.304 | 0.038 | -0.123 | -1.522 | 0.128 |
| d_produ_1 ~ frequ | -0.084 | -0.183 | 0.014 | -0.160 | -1.674 | 0.094 |
| frequ ~~ frequ | 0.498 | 0.347 | 0.649 | 1.000 | 6.467 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8931.665 | 9021.551 | 0.915 | 0.083 | 0.082 |
# parameters of interest
params_lcs_respo_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", "d_respo_1 ~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.464 | NA | NA |
| d_respo_1 ~1 | 0.016 | -0.073 | 0.104 | 0.076 | 0.345 | 0.730 |
| respo_t1 ~1 | 4.018 | 3.846 | 4.190 | 9.242 | 45.793 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.042 | 0.005 | 0.079 | 0.996 | 2.203 | 0.028 |
| respo_t1 ~~ d_respo_1 | -0.016 | -0.037 | 0.006 | -0.198 | -1.448 | 0.148 |
| d_respo_1 ~ sb07_09_t1 | 0.008 | -0.019 | 0.035 | 0.061 | 0.599 | 0.549 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.242 | 2.065 | 2.418 | 1.000 | 24.894 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10462.49 | 10579.73 | 0.937 | 0.064 | 0.071 |
# parameters of interest
params_lcs_respo_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respo_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.463 | NA | NA |
| d_respo_1 ~1 | 0.042 | -0.004 | 0.088 | 0.202 | 1.778 | 0.075 |
| respo_t1 ~1 | 3.623 | 3.527 | 3.719 | 8.179 | 74.216 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.042 | 0.004 | 0.081 | 0.978 | 2.162 | 0.031 |
| respo_t1 ~~ d_respo_1 | -0.018 | -0.043 | 0.006 | -0.199 | -1.452 | 0.147 |
| respo_t1 ~ frequ | -0.025 | -0.118 | 0.068 | -0.041 | -0.534 | 0.593 |
| d_respo_1 ~ frequ | 0.043 | -0.025 | 0.111 | 0.149 | 1.248 | 0.212 |
| frequ ~~ frequ | 0.510 | 0.358 | 0.662 | 1.000 | 6.577 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9197.007 | 9286.893 | 0.968 | 0.055 | 0.046 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", "d_anxie_1 ~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.074 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.674 | NA | NA |
| d_anxie_1 ~1 | 0.038 | -0.169 | 0.245 | 0.068 | 0.360 | 0.719 |
| anxie_t1 ~1 | 4.107 | 3.847 | 4.367 | 4.594 | 30.944 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.309 | 0.145 | 0.473 | 0.984 | 3.696 | 0.000 |
| anxie_t1 ~~ d_anxie_1 | -0.173 | -0.276 | -0.070 | -0.431 | -3.292 | 0.001 |
| d_anxie_1 ~ sb07_10_t1 | 0.047 | -0.009 | 0.103 | 0.126 | 1.644 | 0.100 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.270 | 2.044 | 2.496 | 1.000 | 19.676 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10787.37 | 10904.62 | 0.961 | 0.053 | 0.052 |
# parameters of interest
params_lcs_anxie_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_anxie_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.074 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.677 | NA | NA |
| d_anxie_1 ~1 | 0.214 | 0.124 | 0.304 | 0.375 | 4.646 | 0.000 |
| anxie_t1 ~1 | 2.843 | 2.718 | 2.969 | 3.142 | 44.427 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.319 | 0.137 | 0.501 | 0.980 | 3.442 | 0.001 |
| anxie_t1 ~~ d_anxie_1 | -0.208 | -0.335 | -0.081 | -0.410 | -3.217 | 0.001 |
| anxie_t1 ~ frequ | -0.159 | -0.360 | 0.042 | -0.126 | -1.552 | 0.121 |
| d_anxie_1 ~ frequ | 0.113 | -0.034 | 0.260 | 0.141 | 1.503 | 0.133 |
| frequ ~~ frequ | 0.510 | 0.357 | 0.663 | 1.000 | 6.536 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9036.069 | 9125.955 | 0.966 | 0.069 | 0.051 |
# parameters of interest
params_lcs_depre_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", "d_depre_1 ~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.981 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.577 | NA | NA |
| d_depre_1 ~1 | 0.079 | -0.038 | 0.197 | 0.221 | 1.327 | 0.185 |
| depre_t1 ~1 | 3.832 | 3.641 | 4.023 | 6.261 | 39.321 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.129 | 0.071 | 0.186 | 0.993 | 4.379 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.045 | -0.076 | -0.013 | -0.274 | -2.776 | 0.006 |
| d_depre_1 ~ sb07_11_t1 | 0.020 | -0.012 | 0.051 | 0.086 | 1.208 | 0.227 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.492 | 2.294 | 2.689 | 1.000 | 24.729 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10666.01 | 10783.26 | 0.949 | 0.07 | 0.06 |
# parameters of interest
params_lcs_depre_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_depre_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.976 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.574 | NA | NA |
| d_depre_1 ~1 | 0.150 | 0.088 | 0.212 | 0.409 | 4.730 | 0.000 |
| depre_t1 ~1 | 2.964 | 2.851 | 3.076 | 4.763 | 51.521 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.133 | 0.073 | 0.194 | 0.998 | 4.347 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.057 | -0.092 | -0.022 | -0.251 | -3.184 | 0.001 |
| depre_t1 ~ frequ | -0.007 | -0.132 | 0.118 | -0.008 | -0.109 | 0.913 |
| d_depre_1 ~ frequ | 0.023 | -0.069 | 0.114 | 0.044 | 0.484 | 0.628 |
| frequ ~~ frequ | 0.508 | 0.355 | 0.661 | 1.000 | 6.512 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9180.23 | 9270.115 | 0.984 | 0.042 | 0.031 |
# parameters of interest
params_lcs_volat_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", "d_volat_1 ~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.056 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_volat_1 ~1 | -0.015 | -0.194 | 0.164 | -0.029 | -0.163 | 0.870 |
| volat_t1 ~1 | 2.658 | 2.406 | 2.910 | 3.050 | 20.687 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.262 | 0.171 | 0.354 | 0.977 | 5.639 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.151 | -0.221 | -0.081 | -0.358 | -4.229 | 0.000 |
| d_volat_1 ~ sb07_12_t1 | -0.060 | -0.125 | 0.004 | -0.153 | -1.827 | 0.068 |
| sb07_12_t1 ~~ sb07_12_t1 | 1.743 | 1.565 | 1.921 | 1.000 | 19.231 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10776.76 | 10894.01 | 0.97 | 0.051 | 0.041 |
# parameters of interest
params_lcs_volat_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_volat_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.057 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_volat_1 ~1 | -0.174 | -0.250 | -0.097 | -0.335 | -4.440 | 0.000 |
| volat_t1 ~1 | 3.229 | 3.107 | 3.351 | 3.700 | 51.901 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.266 | 0.177 | 0.355 | 0.988 | 5.846 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.173 | -0.244 | -0.102 | -0.385 | -4.751 | 0.000 |
| volat_t1 ~ frequ | 0.039 | -0.141 | 0.219 | 0.032 | 0.425 | 0.671 |
| d_volat_1 ~ frequ | -0.080 | -0.215 | 0.055 | -0.109 | -1.163 | 0.245 |
| frequ ~~ frequ | 0.503 | 0.351 | 0.655 | 1.000 | 6.480 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8916.543 | 9006.429 | 0.966 | 0.046 | 0.048 |
# parameters of interest
params_lcs_curio_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", "d_curio_1 ~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.407 | NA | NA |
| d_curio_1 ~1 | 0.040 | -0.083 | 0.164 | 0.202 | 0.640 | 0.522 |
| curio_t1 ~1 | 4.301 | 4.120 | 4.482 | 8.839 | 46.576 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.040 | -0.010 | 0.089 | 0.996 | 1.576 | 0.115 |
| curio_t1 ~~ d_curio_1 | -0.017 | -0.049 | 0.015 | -0.178 | -1.019 | 0.308 |
| d_curio_1 ~ sb07_13_t1 | 0.009 | -0.040 | 0.059 | 0.059 | 0.376 | 0.707 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.563 | 1.402 | 1.724 | 1.000 | 18.986 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10522.1 | 10639.34 | 0.963 | 0.046 | 0.052 |
# parameters of interest
params_lcs_curio_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_curio_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.406 | NA | NA |
| d_curio_1 ~1 | 0.065 | 0.012 | 0.118 | 0.325 | 2.407 | 0.016 |
| curio_t1 ~1 | 4.061 | 3.969 | 4.153 | 8.301 | 86.403 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.038 | -0.010 | 0.086 | 0.944 | 1.539 | 0.124 |
| curio_t1 ~~ d_curio_1 | -0.022 | -0.056 | 0.011 | -0.236 | -1.293 | 0.196 |
| curio_t1 ~ frequ | 0.123 | 0.010 | 0.236 | 0.177 | 2.127 | 0.033 |
| d_curio_1 ~ frequ | 0.067 | -0.032 | 0.167 | 0.236 | 1.328 | 0.184 |
| frequ ~~ frequ | 0.495 | 0.345 | 0.646 | 1.000 | 6.440 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_specif_hyp6 <- '
aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1
aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)
aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1
d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2
aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0
aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0
d_aesth_1 ~ 1 # This estimates the intercept of the change score
aesth_t1 ~ 1 # This estimates the intercept of aesth_t1
d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores
aesth_t1 ~~ d_aesth_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
aesth_t1 ~ sb07_14_t1 # This estimates the moderation effect on personality at T1
d_aesth_1 ~ sb07_14_t1 # This estimates the moderation effect on the change score
bf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2
bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2
bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2
bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2
bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1
bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1
bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1
bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1
bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2
bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2
bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2
bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2
bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1
bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1
bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1
bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2
bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2
bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb07_14_t1 ~~ sb07_14_t1
sb07_14_t1 ~ 1
'
fit_mi_lcs_aesth_curr_specif_hyp6 <- lavaan(mi_lcs_aesth_curr_specif_hyp6,
data=df_sbsa_wide_pers_sb %>% filter(!is.na(bf05_05_t1) & !is.na(bf05_05_t2)),
estimator='mlr', fixed.x=FALSE, missing="fiml")Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
is not positive definite;
use lavInspect(fit, "cov.lv") to investigate.# This model did not converge properly with missing data and FIML -> no problem when only using complete data
summary(fit_mi_lcs_aesth_curr_specif_hyp6, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 103 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 32
Number of equality constraints 10
Number of observations 331
Number of missing patterns 2
Model Test User Model:
Standard Scaled
Test Statistic 389.043 367.806
Degrees of freedom 32 32
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.058
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1061.777 828.089
Degrees of freedom 36 36
P-value 0.000 0.000
Scaling correction factor 1.282
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.652 0.576
Tucker-Lewis Index (TLI) 0.608 0.523
Robust Comparative Fit Index (CFI) 0.651
Robust Tucker-Lewis Index (TLI) 0.608
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4500.339 -4500.339
Scaling correction factor 0.866
for the MLR correction
Loglikelihood unrestricted model (H1) -4305.817 -4305.817
Scaling correction factor 1.140
for the MLR correction
Akaike (AIC) 9044.678 9044.678
Bayesian (BIC) 9128.324 9128.324
Sample-size adjusted Bayesian (SABIC) 9058.539 9058.539
Root Mean Square Error of Approximation:
RMSEA 0.184 0.178
90 Percent confidence interval - lower 0.168 0.162
90 Percent confidence interval - upper 0.200 0.194
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
Robust RMSEA 0.183
90 Percent confidence interval - lower 0.167
90 Percent confidence interval - upper 0.200
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.205 0.205
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 =~
b05_05_ 1.000 0.006 0.005
b05_20_ (lmb2) 3.110 2.621 1.187 0.235 0.019 0.018
b05_35_ (lmb3) 3.303 2.803 1.178 0.239 0.020 0.020
b05_50_ (lmb4) -3.443 2.980 -1.155 0.248 -0.021 -0.016
aesth_t2 =~
b05_05_ 1.000 0.159 0.120
b05_20_ (lmb2) 3.110 2.621 1.187 0.235 0.493 0.428
b05_35_ (lmb3) 3.303 2.803 1.178 0.239 0.524 0.465
b05_50_ (lmb4) -3.443 2.980 -1.155 0.248 -0.546 -0.386
d_aesth_1 =~
asth_t2 1.000 0.833 0.833
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t2 ~
aesth_t1 1.000 0.039 0.039
aesth_t1 ~
sb07_14_t1 0.005 0.016 0.289 0.773 0.732 1.000
d_aesth_1 ~
sb07_14_t1 0.016 0.017 0.933 0.351 0.123 0.169
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t1 ~~
.d_aesth_1 0.004 0.006 0.595 0.552 Inf Inf
.bf05_05_t1 ~~
.bf05_05_t2 0.543 0.106 5.128 0.000 0.543 0.314
.bf05_20_t1 ~~
.bf05_20_t2 0.733 0.095 7.686 0.000 0.733 0.674
.bf05_35_t1 ~~
.bf05_35_t2 0.638 0.084 7.577 0.000 0.638 0.642
.bf05_50_t1 ~~
.bf05_50_t2 1.184 0.107 11.087 0.000 1.184 0.694
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t2 0.000 0.000 0.000
.d_aesth_1 -0.038 0.047 -0.818 0.414 -0.291 -0.291
.aesth_t1 2.963 0.075 39.688 0.000 480.641 480.641
.bf05_05_1 0.000 0.000 0.000
.bf05_20_1 (m2) -5.417 7.802 -0.694 0.488 -5.417 -5.194
.bf05_35_1 (m3) -5.978 8.350 -0.716 0.474 -5.978 -5.995
.bf05_50_1 (m4) 12.807 8.880 1.442 0.149 12.807 9.804
.bf05_05_2 0.000 0.000 0.000
.bf05_20_2 (m2) -5.417 7.802 -0.694 0.488 -5.417 -4.696
.bf05_35_2 (m3) -5.978 8.350 -0.716 0.474 -5.978 -5.308
.bf05_50_2 (m4) 12.807 8.880 1.442 0.149 12.807 9.047
sb07_14_1 2.731 0.076 36.109 0.000 2.731 1.999
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.asth_t2 0.000 0.000 0.000
.d_sth_1 0.017 0.030 0.567 0.571 0.972 0.972
.b05_05_ (res1) 1.729 0.073 23.777 0.000 1.729 1.000
.b05_20_ (res2) 1.087 0.085 12.840 0.000 1.087 1.000
.b05_35_ (res3) 0.994 0.076 13.118 0.000 0.994 1.000
.b05_50_ (res4) 1.706 0.090 18.952 0.000 1.706 1.000
.b05_05_ (res1) 1.729 0.073 23.777 0.000 1.729 0.986
.b05_20_ (res2) 1.087 0.085 12.840 0.000 1.087 0.817
.b05_35_ (res3) 0.994 0.076 13.118 0.000 0.994 0.784
.b05_50_ (res4) 1.706 0.090 18.952 0.000 1.706 0.851
s07_14_ 1.866 0.089 21.066 0.000 1.866 1.000
.asth_t1 0.000 0.000 0.000# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 331 | 22 | 9044.678 | 9128.324 | 0.652 | 0.184 | 0.205 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", "d_aesth_1 ~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.039 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.833 | NA | NA |
| d_aesth_1 ~1 | -0.038 | -0.130 | 0.054 | -0.291 | -0.818 | 0.414 |
| aesth_t1 ~1 | 2.963 | 2.817 | 3.110 | 480.641 | 39.688 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.017 | -0.042 | 0.076 | 0.972 | 0.567 | 0.571 |
| aesth_t1 ~~ d_aesth_1 | 0.004 | -0.008 | 0.016 | Inf | 0.595 | 0.552 |
| d_aesth_1 ~ sb07_14_t1 | 0.016 | -0.018 | 0.051 | 0.169 | 0.933 | 0.351 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.866 | 1.692 | 2.040 | 1.000 | 21.066 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_frequ_hyp6 <- '
aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1
aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1
d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2
aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0
aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0
d_aesth_1 ~ 1 # This estimates the intercept of the change score
aesth_t1 ~ 1 # This estimates the intercept of aesth_t1
d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores
aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1
aesth_t1 ~~ d_aesth_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
aesth_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_aesth_1 ~ frequ # This estimates the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
bf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2
bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2
bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2
bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2
bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1
bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1
bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1
bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1
bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2
bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2
bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2
bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2
bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1
bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1
bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1
bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2
bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2
bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
fit_mi_lcs_aesth_curr_frequ_hyp6 <- lavaan(mi_lcs_aesth_curr_frequ_hyp6,
data=df_sbsa_wide_pers_sb_mod %>% filter(!is.na(bf05_05_t1) & !is.na(bf05_05_t2)),
estimator='mlr', fixed.x=FALSE, missing="fiml")
# This model did not converge properly with missing data and FIML -> no problem when only using complete data
summary(fit_mi_lcs_aesth_curr_frequ_hyp6, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 332 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 40
Number of equality constraints 10
Number of observations 331
Number of missing patterns 1
Model Test User Model:
Standard Scaled
Test Statistic 54.322 49.996
Degrees of freedom 47 47
P-value (Chi-square) 0.216 0.355
Scaling correction factor 1.087
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1359.657 1108.430
Degrees of freedom 55 55
P-value 0.000 0.000
Scaling correction factor 1.227
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.994 0.997
Tucker-Lewis Index (TLI) 0.993 0.997
Robust Comparative Fit Index (CFI) 0.998
Robust Tucker-Lewis Index (TLI) 0.998
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -5104.724 -5104.724
Scaling correction factor 0.890
for the MLR correction
Loglikelihood unrestricted model (H1) -5077.563 -5077.563
Scaling correction factor 1.125
for the MLR correction
Akaike (AIC) 10269.448 10269.448
Bayesian (BIC) 10383.511 10383.511
Sample-size adjusted Bayesian (SABIC) 10288.350 10288.350
Root Mean Square Error of Approximation:
RMSEA 0.022 0.014
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.044 0.039
P-value H_0: RMSEA <= 0.050 0.987 0.997
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.012
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.041
P-value H_0: Robust RMSEA <= 0.050 0.993
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.036 0.036
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 =~
b05_05_ 1.000 0.039 0.030
b05_20_ (lmb2) 23.507 1.120 20.996 0.000 0.917 0.826
b05_35_ (lmb3) 22.131 1.180 18.762 0.000 0.863 0.809
b05_50_ (lmb4) -18.112 1.318 -13.740 0.000 -0.706 -0.519
aesth_t2 =~
b05_05_ 1.000 0.040 0.030
b05_20_ (lmb2) 23.507 1.120 20.996 0.000 0.940 0.832
b05_35_ (lmb3) 22.131 1.180 18.762 0.000 0.885 0.816
b05_50_ (lmb4) -18.112 1.318 -13.740 0.000 -0.724 -0.528
frequ =~
s04_01_ 1.000 0.707 0.733
s04_02_ 1.153 0.109 10.625 0.000 0.815 0.711
s04_03_ 1.210 0.109 11.094 0.000 0.856 0.754
d_aesth_1 =~
asth_t2 1.000 0.585 0.585
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t2 ~
aesth_t1 1.000 0.975 0.975
aesth_t1 ~
frequ 0.010 0.004 2.402 0.016 0.190 0.190
d_aesth_1 ~
frequ 0.005 0.003 1.707 0.088 0.156 0.156
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t1 ~~
.d_aesth_1 -0.000 0.000 -2.809 0.005 -0.295 -0.295
.bf05_05_t1 ~~
.bf05_05_t2 0.550 0.106 5.210 0.000 0.550 0.316
.bf05_20_t1 ~~
.bf05_20_t2 0.122 0.065 1.890 0.059 0.122 0.312
.bf05_35_t1 ~~
.bf05_35_t2 0.097 0.059 1.640 0.101 0.097 0.246
.bf05_50_t1 ~~
.bf05_50_t2 0.816 0.099 8.267 0.000 0.816 0.602
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t2 0.000 0.000 0.000
.d_aesth_1 0.002 0.002 0.915 0.360 0.068 0.068
.aesth_t1 2.978 0.059 50.604 0.000 76.378 76.378
frequ 0.000 0.000 0.000
.bf05_05_1 0.000 0.000 0.000
.bf05_20_1 (m2) -66.176 3.110 -21.281 0.000 -66.176 -59.618
.bf05_35_1 (m3) -62.066 3.186 -19.480 0.000 -62.066 -58.182
.bf05_50_1 (m4) 56.503 3.916 14.431 0.000 56.503 41.497
.bf05_05_2 0.000 0.000 0.000
.bf05_20_2 (m2) -66.176 3.110 -21.281 0.000 -66.176 -58.587
.bf05_35_2 (m3) -62.066 3.186 -19.480 0.000 -62.066 -57.215
.bf05_50_2 (m4) 56.503 3.916 14.431 0.000 56.503 41.210
.sb04_01_2 3.480 0.053 65.575 0.000 3.480 3.604
.sb04_02_2 3.036 0.063 48.155 0.000 3.036 2.647
.sb04_03_2 3.423 0.062 54.843 0.000 3.423 3.014
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.asth_t2 0.000 0.000 0.000
.d_sth_1 0.001 0.000 3.904 0.000 0.976 0.976
.asth_t1 0.001 0.000 9.518 0.000 0.964 0.964
frequ 0.500 0.077 6.529 0.000 1.000 1.000
.b05_05_ (res1) 1.742 0.070 24.777 0.000 1.742 0.999
.b05_20_ (res2) 0.392 0.069 5.666 0.000 0.392 0.318
.b05_35_ (res3) 0.393 0.059 6.689 0.000 0.393 0.346
.b05_50_ (res4) 1.355 0.100 13.604 0.000 1.355 0.731
.b05_05_ (res1) 1.742 0.070 24.777 0.000 1.742 0.999
.b05_20_ (res2) 0.392 0.069 5.666 0.000 0.392 0.307
.b05_35_ (res3) 0.393 0.059 6.689 0.000 0.393 0.334
.b05_50_ (res4) 1.355 0.100 13.604 0.000 1.355 0.721
.s04_01_ 0.432 0.055 7.841 0.000 0.432 0.463
.s04_02_ 0.651 0.085 7.633 0.000 0.651 0.495
.s04_03_ 0.556 0.079 7.082 0.000 0.556 0.432# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 331 | 30 | 10269.45 | 10383.51 | 0.994 | 0.022 | 0.036 |
# parameters of interest
params_lcs_aesth_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_aesth_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.585 | NA | NA |
| d_aesth_1 ~1 | 0.002 | -0.002 | 0.005 | 0.068 | 0.915 | 0.360 |
| aesth_t1 ~1 | 2.978 | 2.863 | 3.093 | 76.378 | 50.604 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.976 | 3.904 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | -0.295 | -2.809 | 0.005 |
| aesth_t1 ~ frequ | 0.010 | 0.002 | 0.019 | 0.190 | 2.402 | 0.016 |
| d_aesth_1 ~ frequ | 0.005 | -0.001 | 0.011 | 0.156 | 1.707 | 0.088 |
| frequ ~~ frequ | 0.500 | 0.350 | 0.651 | 1.000 | 6.529 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8796.754 | 8886.64 | 0.941 | 0.073 | 0.068 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", "d_imagi_1 ~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.472 | NA | NA |
| d_imagi_1 ~1 | -0.046 | -0.200 | 0.108 | -0.131 | -0.586 | 0.558 |
| imagi_t1 ~1 | 3.956 | 3.749 | 4.163 | 5.497 | 37.456 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.119 | 0.040 | 0.198 | 0.966 | 2.942 | 0.003 |
| imagi_t1 ~~ d_imagi_1 | -0.033 | -0.096 | 0.029 | -0.138 | -1.046 | 0.296 |
| d_imagi_1 ~ sb07_15_t1 | 0.046 | -0.001 | 0.092 | 0.183 | 1.926 | 0.054 |
| sb07_15_t1 ~~ sb07_15_t1 | 1.982 | 1.807 | 2.157 | 1.000 | 22.152 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 30 | 10317.77 | 10435.01 | 0.965 | 0.051 | 0.055 |
# parameters of interest
params_lcs_imagi_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_imagi_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.971 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.473 | NA | NA |
| d_imagi_1 ~1 | 0.098 | 0.034 | 0.163 | 0.279 | 2.982 | 0.003 |
| imagi_t1 ~1 | 3.568 | 3.470 | 3.665 | 4.947 | 71.846 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.121 | 0.040 | 0.203 | 0.979 | 2.913 | 0.004 |
| imagi_t1 ~~ d_imagi_1 | -0.054 | -0.120 | 0.011 | -0.223 | -1.628 | 0.104 |
| imagi_t1 ~ frequ | 0.241 | 0.086 | 0.395 | 0.234 | 3.050 | 0.002 |
| d_imagi_1 ~ frequ | 0.072 | -0.038 | 0.182 | 0.144 | 1.285 | 0.199 |
| frequ ~~ frequ | 0.491 | 0.342 | 0.641 | 1.000 | 6.455 | 0.000 |
Gather all relevant effects:
# create df for table:
# main effects (change score)
df_table_hyp6_main <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~1 "),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~1 "),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~1 "),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~1 ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_main = estimate, std_main = std.all, p_main = p.value)
# moderation effects
df_table_hyp6_mod <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_mod = estimate, std_mod = std.all, p_mod = p.value)
# combine
df_table_hyp6 <- df_table_hyp6_main %>% left_join(df_table_hyp6_mod)Results summary across the Big Five traits: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6[1:10, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| extraversion | goals | 0.532 | 1.511 | 0.001 | 0.063 | 0.128 | 0.324 |
| extraversion | frequency | 0.703 | 2.021 | 0.000 | 0.076 | 0.153 | 0.090 |
| agreeableness | goals | 0.655 | 3.090 | 0.000 | 0.022 | 0.090 | 0.362 |
| agreeableness | frequency | 0.778 | 3.592 | 0.000 | 0.073 | 0.243 | 0.016 |
| conscientiousness | goals | 0.662 | 1.845 | 0.000 | 0.002 | 0.008 | 0.936 |
| conscientiousness | frequency | 0.714 | 1.972 | 0.000 | 0.090 | 0.176 | 0.018 |
| neuroticism | goals | 0.808 | 1.622 | 0.000 | 0.085 | 0.198 | 0.071 |
| neuroticism | frequency | 0.531 | 1.080 | 0.000 | -0.066 | -0.096 | 0.200 |
| openness | goals | -0.076 | -0.389 | 0.627 | 0.103 | 0.314 | 0.007 |
| openness | frequency | 0.161 | 0.828 | 0.287 | 0.075 | 0.271 | 0.028 |
Results summary across the Big Five facets: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6[11:40, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| sociability | goals | 0.832 | 1.560 | 0.000 | -0.003 | -0.008 | 0.920 |
| sociability | frequency | 0.829 | 1.578 | 0.000 | 0.042 | 0.057 | 0.503 |
| assertiveness | goals | 0.172 | 0.683 | 0.428 | 0.030 | 0.136 | 0.248 |
| assertiveness | frequency | 0.360 | 1.473 | 0.035 | 0.071 | 0.206 | 0.129 |
| energy | goals | 0.332 | 1.603 | 0.021 | -0.001 | -0.004 | 0.964 |
| energy | frequency | 0.369 | 1.697 | 0.016 | -0.050 | -0.160 | 0.174 |
| compassion | goals | 0.759 | 3.028 | 0.009 | 0.017 | 0.096 | 0.475 |
| compassion | frequency | 0.947 | 3.805 | 0.000 | 0.127 | 0.369 | 0.010 |
| respectfulness | goals | 1.019 | 3.062 | 0.001 | 0.032 | 0.130 | 0.162 |
| respectfulness | frequency | 1.263 | 3.870 | 0.000 | 0.053 | 0.116 | 0.196 |
| trust | goals | 0.399 | 1.224 | 0.048 | -0.006 | -0.026 | 0.786 |
| trust | frequency | 0.422 | 1.310 | 0.032 | -0.080 | -0.178 | 0.125 |
| organization | goals | 0.417 | 0.778 | 0.000 | -0.005 | -0.015 | 0.883 |
| organization | frequency | 0.462 | 0.897 | 0.000 | -0.099 | -0.138 | 0.106 |
| productiveness | goals | 0.411 | 1.088 | 0.009 | -0.010 | -0.034 | 0.722 |
| productiveness | frequency | 0.447 | 1.195 | 0.006 | -0.108 | -0.204 | 0.029 |
| responsibility | goals | 0.434 | 2.121 | 0.082 | -0.005 | -0.038 | 0.738 |
| responsibility | frequency | 0.377 | 1.813 | 0.050 | 0.041 | 0.141 | 0.221 |
| anxiety | goals | 1.403 | 2.502 | 0.000 | -0.069 | -0.187 | 0.073 |
| anxiety | frequency | 0.948 | 1.661 | 0.000 | 0.072 | 0.090 | 0.316 |
| depression | goals | 0.908 | 2.523 | 0.001 | -0.036 | -0.160 | 0.158 |
| depression | frequency | 0.587 | 1.605 | 0.000 | 0.022 | 0.042 | 0.626 |
| volatility | goals | 0.578 | 1.115 | 0.000 | -0.012 | -0.030 | 0.717 |
| volatility | frequency | 0.560 | 1.080 | 0.000 | -0.071 | -0.097 | 0.256 |
| curiosity | goals | 0.364 | 1.821 | 0.246 | 0.003 | 0.016 | 0.924 |
| curiosity | frequency | 0.453 | 2.259 | 0.077 | 0.079 | 0.277 | 0.125 |
| aesthetic | goals | 0.457 | 17.231 | 0.006 | 0.003 | 0.144 | 0.044 |
| aesthetic | frequency | 0.533 | 22.761 | 0.003 | 0.007 | 0.213 | 0.023 |
| imagination | goals | 0.224 | 0.639 | 0.385 | 0.037 | 0.150 | 0.118 |
| imagination | frequency | 0.492 | 1.400 | 0.034 | 0.099 | 0.197 | 0.086 |
Prepare data frame for plotting:
df_table_hyp6_plot <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp6_plot <- df_table_hyp6_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("current", levels = c("current"), labels = c("current"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp6_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Desire to change and frequency of self-acceptance behaviors measured at the follow-up assessment will be positively related to change in ideal-self ratings in the self-acceptance group.
To test this hypothesis, we will estimate the mean-level difference in ideal trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to accept themselves on a given big five domain or facet. The second will indicate their frequency of self-acceptance behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sa_mod <- df_sbsa %>%
filter(rando=="Self-Acceptance") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07", # facet-specific acceptance goals
"sa04"))) %>% # frequency self-acceptance behaviors
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sa07", "sa04")))) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2,
sa04_01_t1, sa04_02_t1, sa04_03_t1)) # frequency of self-acceptance behaviors measured at T2
# colnames(df_sbsa_wide_pers_sa_mod)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sa_mod <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sa_mod)Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goals
trait_template_mod_goal_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~~ d_trait_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
trait_t1 ~ goals # This estimates the moderation effect on personality at T1
d_trait_1 ~ goals # This estimates the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
trait_facets_nrs <- list(a1 = c(1:3), b2 = c(4:6), c3 = c(7:9), d4 = c(10:12), e5 = c(13:15)) # matching facet nrs to traits
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
mod_names = paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2], "ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), trait_model_fit))
}
# 2nd, for frequency of self-acceptance behavior
trait_template_mod_frequ_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~~ d_trait_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
trait_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_trait_1 ~ frequ # This estimates the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), trait_model_fit))
} Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6601.596 | 6701.678 | 0.98 | 0.036 | 0.04 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.903 | NA | NA |
| d_extra_1 ~1 | -0.038 | -0.097 | 0.021 | -0.096 | -1.262 | 0.207 |
| extra_t1 ~1 | 3.713 | 3.652 | 3.775 | 7.934 | 118.215 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.154 | 0.084 | 0.225 | 0.990 | 4.293 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.091 | -0.139 | -0.043 | -0.497 | -3.731 | 0.000 |
| extra_t1 ~ goals | 0.013 | -0.080 | 0.106 | 0.026 | 0.277 | 0.782 |
| d_extra_1 ~ goals | -0.043 | -0.139 | 0.053 | -0.100 | -0.873 | 0.383 |
| goals ~~ goals | 0.845 | 0.451 | 1.238 | 1.000 | 4.209 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6005.376 | 6105.459 | 0.982 | 0.038 | 0.045 |
# parameters of interest
params_lcs_extra_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_extra_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.074 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.902 | NA | NA |
| d_extra_1 ~1 | -0.037 | -0.094 | 0.021 | -0.096 | -1.255 | 0.210 |
| extra_t1 ~1 | 3.713 | 3.652 | 3.774 | 8.114 | 118.930 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.148 | 0.081 | 0.214 | 0.999 | 4.358 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.088 | -0.135 | -0.042 | -0.511 | -3.700 | 0.000 |
| extra_t1 ~ frequ | 0.102 | 0.008 | 0.195 | 0.169 | 2.133 | 0.033 |
| d_extra_1 ~ frequ | 0.011 | -0.072 | 0.095 | 0.023 | 0.269 | 0.788 |
| frequ ~~ frequ | 0.582 | 0.393 | 0.771 | 1.000 | 6.033 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6447.578 | 6547.661 | 0.99 | 0.033 | 0.041 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.988 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.674 | NA | NA |
| d_agree_1 ~1 | -0.012 | -0.057 | 0.034 | -0.037 | -0.511 | 0.609 |
| agree_t1 ~1 | 3.705 | 3.631 | 3.779 | 7.923 | 98.601 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.101 | 0.062 | 0.141 | 0.994 | 4.997 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.047 | -0.077 | -0.017 | -0.318 | -3.097 | 0.002 |
| agree_t1 ~ goals | -0.054 | -0.164 | 0.056 | -0.082 | -0.957 | 0.339 |
| d_agree_1 ~ goals | 0.034 | -0.063 | 0.131 | 0.076 | 0.688 | 0.491 |
| goals ~~ goals | 0.502 | 0.181 | 0.824 | 1.000 | 3.063 | 0.002 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 5994.798 | 6094.88 | 0.979 | 0.051 | 0.056 |
# parameters of interest
params_lcs_agree_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_agree_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.676 | NA | NA |
| d_agree_1 ~1 | -0.011 | -0.056 | 0.034 | -0.035 | -0.486 | 0.627 |
| agree_t1 ~1 | 3.705 | 3.631 | 3.778 | 8.075 | 98.699 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.098 | 0.059 | 0.136 | 0.993 | 4.929 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.051 | -0.081 | -0.021 | -0.376 | -3.308 | 0.001 |
| agree_t1 ~ frequ | 0.205 | 0.120 | 0.291 | 0.342 | 4.722 | 0.000 |
| d_agree_1 ~ frequ | 0.034 | -0.030 | 0.097 | 0.082 | 1.029 | 0.304 |
| frequ ~~ frequ | 0.582 | 0.400 | 0.764 | 1.000 | 6.257 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6093.112 | 6193.194 | 0.999 | 0.01 | 0.033 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.045 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.738 | NA | NA |
| d_consc_1 ~1 | 0.017 | -0.031 | 0.066 | 0.050 | 0.700 | 0.484 |
| consc_t1 ~1 | 4.377 | 4.319 | 4.435 | 8.899 | 148.438 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.121 | 0.059 | 0.183 | 1.000 | 3.810 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.070 | -0.134 | -0.007 | -0.418 | -2.175 | 0.030 |
| consc_t1 ~ goals | -0.066 | -0.120 | -0.012 | -0.151 | -2.401 | 0.016 |
| d_consc_1 ~ goals | 0.000 | -0.043 | 0.042 | -0.001 | -0.019 | 0.985 |
| goals ~~ goals | 1.264 | 0.955 | 1.572 | 1.000 | 8.025 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 5601.08 | 5701.162 | 0.988 | 0.036 | 0.04 |
# parameters of interest
params_lcs_consc_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_consc_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.045 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.737 | NA | NA |
| d_consc_1 ~1 | 0.018 | -0.031 | 0.067 | 0.051 | 0.713 | 0.476 |
| consc_t1 ~1 | 4.377 | 4.319 | 4.435 | 8.851 | 148.369 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.121 | 0.059 | 0.183 | 0.998 | 3.842 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.074 | -0.135 | -0.012 | -0.448 | -2.333 | 0.020 |
| consc_t1 ~ frequ | 0.198 | 0.111 | 0.285 | 0.301 | 4.476 | 0.000 |
| d_consc_1 ~ frequ | 0.022 | -0.052 | 0.097 | 0.048 | 0.586 | 0.558 |
| frequ ~~ frequ | 0.566 | 0.382 | 0.749 | 1.000 | 6.054 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6247.27 | 6347.353 | 0.971 | 0.049 | 0.049 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.938 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.780 | NA | NA |
| d_neuro_1 ~1 | 0.011 | -0.037 | 0.059 | 0.034 | 0.445 | 0.656 |
| neuro_t1 ~1 | 1.646 | 1.595 | 1.697 | 4.228 | 63.227 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.103 | 0.054 | 0.151 | 0.981 | 4.166 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.042 | -0.070 | -0.014 | -0.338 | -2.930 | 0.003 |
| neuro_t1 ~ goals | 0.003 | -0.056 | 0.062 | 0.006 | 0.105 | 0.917 |
| d_neuro_1 ~ goals | 0.056 | -0.019 | 0.130 | 0.136 | 1.462 | 0.144 |
| goals ~~ goals | 0.632 | 0.175 | 1.089 | 1.000 | 2.713 | 0.007 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 5708.413 | 5808.495 | 0.986 | 0.035 | 0.045 |
# parameters of interest
params_lcs_neuro_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_neuro_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.937 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.781 | NA | NA |
| d_neuro_1 ~1 | 0.011 | -0.038 | 0.059 | 0.032 | 0.427 | 0.669 |
| neuro_t1 ~1 | 1.646 | 1.595 | 1.697 | 4.183 | 63.132 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.107 | 0.056 | 0.158 | 0.999 | 4.123 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.044 | -0.073 | -0.015 | -0.346 | -2.938 | 0.003 |
| neuro_t1 ~ frequ | -0.092 | -0.169 | -0.015 | -0.178 | -2.341 | 0.019 |
| d_neuro_1 ~ frequ | -0.016 | -0.086 | 0.053 | -0.038 | -0.465 | 0.642 |
| frequ ~~ frequ | 0.583 | 0.391 | 0.774 | 1.000 | 5.968 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6392.341 | 6492.424 | 0.99 | 0.033 | 0.033 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.966 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.638 | NA | NA |
| d_openn_1 ~1 | -0.013 | -0.053 | 0.026 | -0.057 | -0.665 | 0.506 |
| openn_t1 ~1 | 3.811 | 3.752 | 3.870 | 10.652 | 126.434 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.056 | 0.028 | 0.083 | 0.996 | 3.978 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.023 | -0.039 | -0.006 | -0.271 | -2.704 | 0.007 |
| openn_t1 ~ goals | -0.071 | -0.138 | -0.004 | -0.148 | -2.072 | 0.038 |
| d_openn_1 ~ goals | 0.019 | -0.037 | 0.075 | 0.061 | 0.676 | 0.499 |
| goals ~~ goals | 0.562 | 0.340 | 0.783 | 1.000 | 4.977 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 26 | 6011.516 | 6111.598 | 0.99 | 0.03 | 0.037 |
# parameters of interest
params_lcs_openn_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_openn_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.966 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.639 | NA | NA |
| d_openn_1 ~1 | -0.013 | -0.053 | 0.027 | -0.056 | -0.650 | 0.515 |
| openn_t1 ~1 | 3.811 | 3.752 | 3.870 | 10.633 | 126.454 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.056 | 0.029 | 0.083 | 0.996 | 4.015 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.024 | -0.041 | -0.008 | -0.294 | -2.913 | 0.004 |
| openn_t1 ~ frequ | 0.085 | 0.017 | 0.152 | 0.181 | 2.456 | 0.014 |
| d_openn_1 ~ frequ | 0.020 | -0.037 | 0.077 | 0.065 | 0.686 | 0.493 |
| frequ ~~ frequ | 0.588 | 0.400 | 0.777 | 1.000 | 6.128 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goal
facet_template_mod_goal_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1
d_facet_1 ~ ind_goal # This estimates the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
mod_name = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), facet_model_fit))
}
# 2nd, for frequency of self-acceptance behavior
facet_template_mod_frequ_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
facet_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_facet_1 ~ frequ # This estimates the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), facet_model_fit))
} Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8327.268 | 8415.803 | 0.956 | 0.047 | 0.048 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", "d_socia_1 ~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.744 | NA | NA |
| d_socia_1 ~1 | -0.005 | -0.136 | 0.125 | -0.020 | -0.078 | 0.938 |
| socia_t1 ~1 | 4.230 | 4.071 | 4.390 | 11.776 | 51.999 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.064 | 0.018 | 0.111 | 0.992 | 2.706 | 0.007 |
| socia_t1 ~~ d_socia_1 | -0.037 | -0.062 | -0.013 | -0.412 | -2.955 | 0.003 |
| d_socia_1 ~ sa07_01_t1 | -0.017 | -0.055 | 0.022 | -0.090 | -0.839 | 0.401 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.904 | 1.711 | 2.098 | 1.000 | 19.272 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10014.04 | 10129.52 | 0.973 | 0.035 | 0.049 |
# parameters of interest
params_lcs_socia_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_socia_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.050 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.748 | NA | NA |
| d_socia_1 ~1 | -0.058 | -0.106 | -0.009 | -0.229 | -2.327 | 0.020 |
| socia_t1 ~1 | 4.321 | 4.249 | 4.392 | 12.216 | 118.396 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.063 | 0.017 | 0.109 | 0.993 | 2.666 | 0.008 |
| socia_t1 ~~ d_socia_1 | -0.038 | -0.063 | -0.012 | -0.424 | -2.922 | 0.003 |
| socia_t1 ~ frequ | -0.007 | -0.087 | 0.074 | -0.015 | -0.168 | 0.866 |
| d_socia_1 ~ frequ | -0.028 | -0.099 | 0.043 | -0.084 | -0.767 | 0.443 |
| frequ ~~ frequ | 0.576 | 0.390 | 0.761 | 1.000 | 6.089 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8585.716 | 8674.25 | 0.998 | 0.01 | 0.034 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", "d_asser_1 ~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.010 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.748 | NA | NA |
| d_asser_1 ~1 | 0.028 | -0.070 | 0.125 | 0.135 | 0.553 | 0.580 |
| asser_t1 ~1 | 4.106 | 3.976 | 4.235 | 14.922 | 61.986 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.041 | 0.004 | 0.078 | 0.983 | 2.151 | 0.031 |
| asser_t1 ~~ d_asser_1 | -0.021 | -0.043 | 0.002 | -0.376 | -1.828 | 0.068 |
| d_asser_1 ~ sa07_02_t1 | -0.019 | -0.047 | 0.008 | -0.132 | -1.398 | 0.162 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.902 | 1.715 | 2.088 | 1.000 | 19.966 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10254.65 | 10370.13 | 0.994 | 0.015 | 0.035 |
# parameters of interest
params_lcs_asser_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_asser_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.008 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.742 | NA | NA |
| d_asser_1 ~1 | -0.033 | -0.071 | 0.004 | -0.164 | -1.744 | 0.081 |
| asser_t1 ~1 | 4.163 | 4.072 | 4.253 | 15.106 | 89.805 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.040 | 0.004 | 0.076 | 0.969 | 2.164 | 0.030 |
| asser_t1 ~~ d_asser_1 | -0.023 | -0.047 | 0.000 | -0.435 | -1.941 | 0.052 |
| asser_t1 ~ frequ | 0.081 | 0.008 | 0.155 | 0.221 | 2.163 | 0.031 |
| d_asser_1 ~ frequ | 0.047 | -0.007 | 0.102 | 0.175 | 1.695 | 0.090 |
| frequ ~~ frequ | 0.563 | 0.378 | 0.747 | 1.000 | 5.973 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7951.326 | 8039.86 | 0.912 | 0.05 | 0.06 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", "d_energ_1 ~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.105 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.930 | NA | NA |
| d_energ_1 ~1 | -0.126 | -0.291 | 0.039 | -0.532 | -1.492 | 0.136 |
| energ_t1 ~1 | 2.473 | 2.309 | 2.637 | 8.790 | 29.552 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.054 | -0.030 | 0.138 | 0.956 | 1.251 | 0.211 |
| energ_t1 ~~ d_energ_1 | -0.033 | -0.093 | 0.026 | -0.517 | -1.096 | 0.273 |
| d_energ_1 ~ sa07_03_t1 | 0.035 | -0.008 | 0.079 | 0.209 | 1.599 | 0.110 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.954 | 1.765 | 2.144 | 1.000 | 20.241 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 9607.103 | 9722.583 | 0.94 | 0.046 | 0.057 |
# parameters of interest
params_lcs_energ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_energ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.081 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.911 | NA | NA |
| d_energ_1 ~1 | -0.011 | -0.054 | 0.033 | -0.047 | -0.476 | 0.634 |
| energ_t1 ~1 | 2.391 | 2.275 | 2.507 | 8.863 | 40.394 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.052 | -0.028 | 0.131 | 1.000 | 1.273 | 0.203 |
| energ_t1 ~~ d_energ_1 | -0.031 | -0.088 | 0.025 | -0.540 | -1.078 | 0.281 |
| energ_t1 ~ frequ | -0.120 | -0.200 | -0.041 | -0.338 | -2.964 | 0.003 |
| d_energ_1 ~ frequ | -0.001 | -0.057 | 0.055 | -0.004 | -0.040 | 0.968 |
| frequ ~~ frequ | 0.576 | 0.395 | 0.758 | 1.000 | 6.231 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8928.646 | 9017.181 | 1 | 0 | 0.032 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", "d_compa_1 ~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.019 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.592 | NA | NA |
| d_compa_1 ~1 | 0.077 | -0.062 | 0.216 | 0.231 | 1.088 | 0.277 |
| compa_t1 ~1 | 4.268 | 4.101 | 4.435 | 7.409 | 50.018 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.108 | 0.003 | 0.212 | 0.962 | 2.025 | 0.043 |
| compa_t1 ~~ d_compa_1 | -0.063 | -0.133 | 0.008 | -0.331 | -1.734 | 0.083 |
| d_compa_1 ~ sa07_04_t1 | -0.050 | -0.106 | 0.006 | -0.194 | -1.757 | 0.079 |
| sa07_04_t1 ~~ sa07_04_t1 | 1.690 | 1.511 | 1.870 | 1.000 | 18.458 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10637.48 | 10752.96 | 0.975 | 0.032 | 0.052 |
# parameters of interest
params_lcs_compa_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_compa_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.587 | NA | NA |
| d_compa_1 ~1 | -0.041 | -0.113 | 0.032 | -0.119 | -1.101 | 0.271 |
| compa_t1 ~1 | 4.255 | 4.163 | 4.348 | 7.223 | 90.296 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.113 | 0.004 | 0.222 | 0.970 | 2.028 | 0.043 |
| compa_t1 ~~ d_compa_1 | -0.050 | -0.118 | 0.018 | -0.270 | -1.430 | 0.153 |
| compa_t1 ~ frequ | 0.290 | 0.144 | 0.436 | 0.374 | 3.899 | 0.000 |
| d_compa_1 ~ frequ | -0.078 | -0.210 | 0.053 | -0.174 | -1.164 | 0.244 |
| frequ ~~ frequ | 0.578 | 0.391 | 0.764 | 1.000 | 6.081 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7190.198 | 7278.733 | 0.934 | 0.058 | 0.057 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", "d_respe_1 ~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.663 | NA | NA |
| d_respe_1 ~1 | -0.015 | -0.093 | 0.063 | -0.062 | -0.372 | 0.710 |
| respe_t1 ~1 | 4.752 | 4.669 | 4.835 | 13.912 | 112.525 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.056 | 0.014 | 0.099 | 0.996 | 2.594 | 0.009 |
| respe_t1 ~~ d_respe_1 | -0.022 | -0.054 | 0.011 | -0.271 | -1.318 | 0.187 |
| d_respe_1 ~ sa07_05_t1 | 0.011 | -0.027 | 0.050 | 0.061 | 0.580 | 0.562 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.620 | 1.354 | 1.887 | 1.000 | 11.922 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 8898.344 | 9013.824 | 0.963 | 0.043 | 0.049 |
# parameters of interest
params_lcs_respe_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respe_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.951 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.662 | NA | NA |
| d_respe_1 ~1 | 0.007 | -0.038 | 0.052 | 0.030 | 0.313 | 0.754 |
| respe_t1 ~1 | 4.672 | 4.613 | 4.731 | 13.620 | 156.370 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.055 | 0.013 | 0.098 | 0.973 | 2.534 | 0.011 |
| respe_t1 ~~ d_respe_1 | -0.027 | -0.060 | 0.006 | -0.357 | -1.617 | 0.106 |
| respe_t1 ~ frequ | 0.160 | 0.090 | 0.229 | 0.350 | 4.482 | 0.000 |
| d_respe_1 ~ frequ | 0.052 | -0.010 | 0.115 | 0.165 | 1.639 | 0.101 |
| frequ ~~ frequ | 0.565 | 0.387 | 0.743 | 1.000 | 6.221 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8579.564 | 8668.098 | 0.958 | 0.046 | 0.042 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", "d_trust_1 ~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.642 | NA | NA |
| d_trust_1 ~1 | -0.008 | -0.119 | 0.103 | -0.029 | -0.144 | 0.886 |
| trust_t1 ~1 | 1.967 | 1.828 | 2.107 | 4.564 | 27.678 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.079 | 0.028 | 0.130 | 0.995 | 3.056 | 0.002 |
| trust_t1 ~~ d_trust_1 | -0.035 | -0.071 | 0.001 | -0.293 | -1.930 | 0.054 |
| d_trust_1 ~ sa07_06_t1 | 0.015 | -0.026 | 0.055 | 0.072 | 0.711 | 0.477 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.934 | 1.748 | 2.120 | 1.000 | 20.380 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10243.41 | 10358.89 | 0.925 | 0.062 | 0.06 |
# parameters of interest
params_lcs_trust_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_trust_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.643 | NA | NA |
| d_trust_1 ~1 | 0.029 | -0.027 | 0.085 | 0.100 | 1.019 | 0.308 |
| trust_t1 ~1 | 1.905 | 1.811 | 2.000 | 4.259 | 39.564 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.084 | 0.032 | 0.136 | 0.992 | 3.147 | 0.002 |
| trust_t1 ~~ d_trust_1 | -0.043 | -0.082 | -0.005 | -0.345 | -2.194 | 0.028 |
| trust_t1 ~ frequ | -0.153 | -0.264 | -0.042 | -0.262 | -2.712 | 0.007 |
| d_trust_1 ~ frequ | -0.034 | -0.121 | 0.052 | -0.090 | -0.776 | 0.438 |
| frequ ~~ frequ | 0.585 | 0.396 | 0.773 | 1.000 | 6.086 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7505.15 | 7593.684 | 0.941 | 0.049 | 0.049 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", "d_organ_1 ~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.825 | NA | NA |
| d_organ_1 ~1 | 0.026 | -0.042 | 0.094 | 0.122 | 0.743 | 0.458 |
| organ_t1 ~1 | 1.599 | 1.479 | 1.719 | 6.026 | 26.171 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.044 | 0.003 | 0.085 | 0.996 | 2.119 | 0.034 |
| organ_t1 ~~ d_organ_1 | -0.024 | -0.045 | -0.003 | -0.439 | -2.218 | 0.027 |
| d_organ_1 ~ sa07_07_t1 | -0.008 | -0.032 | 0.015 | -0.060 | -0.704 | 0.482 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.291 | 2.110 | 2.471 | 1.000 | 24.892 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 9099.481 | 9214.961 | 0.971 | 0.035 | 0.044 |
# parameters of interest
params_lcs_organ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_organ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.831 | NA | NA |
| d_organ_1 ~1 | 0.001 | -0.030 | 0.032 | 0.005 | 0.061 | 0.951 |
| organ_t1 ~1 | 1.696 | 1.607 | 1.785 | 6.932 | 37.384 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.038 | 0.002 | 0.074 | 0.977 | 2.062 | 0.039 |
| organ_t1 ~~ d_organ_1 | -0.024 | -0.045 | -0.002 | -0.521 | -2.186 | 0.029 |
| organ_t1 ~ frequ | -0.101 | -0.163 | -0.039 | -0.308 | -3.210 | 0.001 |
| d_organ_1 ~ frequ | -0.040 | -0.090 | 0.011 | -0.151 | -1.537 | 0.124 |
| frequ ~~ frequ | 0.554 | 0.376 | 0.732 | 1.000 | 6.089 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 6971.356 | 7059.89 | 0.879 | 0.068 | 0.065 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", "d_produ_1 ~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.742 | NA | NA |
| d_produ_1 ~1 | -0.018 | -0.130 | 0.093 | -0.068 | -0.324 | 0.746 |
| produ_t1 ~1 | 1.572 | 1.446 | 1.698 | 3.972 | 24.500 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.073 | 0.012 | 0.134 | 1.000 | 2.360 | 0.018 |
| produ_t1 ~~ d_produ_1 | -0.048 | -0.098 | 0.001 | -0.451 | -1.907 | 0.057 |
| d_produ_1 ~ sa07_08_t1 | -0.002 | -0.033 | 0.028 | -0.012 | -0.150 | 0.881 |
| sa07_08_t1 ~~ sa07_08_t1 | 2.019 | 1.832 | 2.206 | 1.000 | 21.144 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 8621.049 | 8736.528 | 0.944 | 0.047 | 0.057 |
# parameters of interest
params_lcs_produ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_produ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.080 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.739 | NA | NA |
| d_produ_1 ~1 | -0.026 | -0.077 | 0.025 | -0.097 | -0.998 | 0.318 |
| produ_t1 ~1 | 1.612 | 1.536 | 1.689 | 4.097 | 41.320 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.071 | 0.012 | 0.129 | 0.972 | 2.348 | 0.019 |
| produ_t1 ~~ d_produ_1 | -0.050 | -0.100 | -0.001 | -0.491 | -2.005 | 0.045 |
| produ_t1 ~ frequ | -0.091 | -0.164 | -0.018 | -0.176 | -2.456 | 0.014 |
| d_produ_1 ~ frequ | -0.059 | -0.136 | 0.018 | -0.167 | -1.510 | 0.131 |
| frequ ~~ frequ | 0.579 | 0.391 | 0.767 | 1.000 | 6.037 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7947.793 | 8036.328 | 0.982 | 0.028 | 0.043 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", "d_respo_1 ~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.787 | NA | NA |
| d_respo_1 ~1 | 0.045 | -0.091 | 0.181 | 0.120 | 0.648 | 0.517 |
| respo_t1 ~1 | 4.364 | 4.198 | 4.530 | 8.693 | 51.651 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.141 | 0.032 | 0.249 | 1.000 | 2.547 | 0.011 |
| respo_t1 ~~ d_respo_1 | -0.083 | -0.154 | -0.012 | -0.443 | -2.303 | 0.021 |
| d_respo_1 ~ sa07_09_t1 | -0.006 | -0.051 | 0.040 | -0.021 | -0.240 | 0.811 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.074 | 1.885 | 2.262 | 1.000 | 21.609 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 9589.959 | 9705.439 | 0.976 | 0.033 | 0.046 |
# parameters of interest
params_lcs_respo_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respo_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.051 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.789 | NA | NA |
| d_respo_1 ~1 | 0.030 | -0.034 | 0.095 | 0.080 | 0.920 | 0.358 |
| respo_t1 ~1 | 4.275 | 4.175 | 4.375 | 8.510 | 83.578 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.142 | 0.036 | 0.248 | 1.000 | 2.616 | 0.009 |
| respo_t1 ~~ d_respo_1 | -0.083 | -0.153 | -0.014 | -0.460 | -2.340 | 0.019 |
| respo_t1 ~ frequ | 0.190 | 0.078 | 0.303 | 0.287 | 3.317 | 0.001 |
| d_respo_1 ~ frequ | 0.004 | -0.089 | 0.098 | 0.009 | 0.090 | 0.929 |
| frequ ~~ frequ | 0.573 | 0.386 | 0.761 | 1.000 | 5.980 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8564.163 | 8652.697 | 0.9 | 0.046 | 0.047 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", "d_anxie_1 ~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.859 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.766 | NA | NA |
| d_anxie_1 ~1 | -0.049 | -0.192 | 0.094 | -0.192 | -0.673 | 0.501 |
| anxie_t1 ~1 | 4.536 | 4.415 | 4.657 | 15.841 | 73.377 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.065 | -0.029 | 0.160 | 1.000 | 1.352 | 0.176 |
| anxie_t1 ~~ d_anxie_1 | -0.018 | -0.057 | 0.021 | -0.247 | -0.894 | 0.372 |
| d_anxie_1 ~ sa07_10_t1 | 0.001 | -0.044 | 0.046 | 0.006 | 0.043 | 0.966 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.097 | 1.913 | 2.282 | 1.000 | 22.295 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10207.23 | 10322.71 | 0.921 | 0.048 | 0.056 |
# parameters of interest
params_lcs_anxie_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_anxie_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.875 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.737 | NA | NA |
| d_anxie_1 ~1 | -0.047 | -0.105 | 0.011 | -0.193 | -1.601 | 0.109 |
| anxie_t1 ~1 | 4.568 | 4.503 | 4.633 | 15.703 | 138.509 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.060 | -0.037 | 0.157 | 0.997 | 1.205 | 0.228 |
| anxie_t1 ~~ d_anxie_1 | -0.016 | -0.053 | 0.021 | -0.234 | -0.833 | 0.405 |
| anxie_t1 ~ frequ | 0.116 | 0.029 | 0.202 | 0.304 | 2.631 | 0.009 |
| d_anxie_1 ~ frequ | -0.018 | -0.106 | 0.071 | -0.055 | -0.387 | 0.699 |
| frequ ~~ frequ | 0.585 | 0.395 | 0.775 | 1.000 | 6.030 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7032.067 | 7120.602 | 0.83 | 0.082 | 0.072 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", "d_depre_1 ~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.062 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.811 | NA | NA |
| d_depre_1 ~1 | -0.021 | -0.108 | 0.066 | -0.095 | -0.474 | 0.635 |
| depre_t1 ~1 | 4.482 | 4.381 | 4.584 | 15.397 | 86.460 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.049 | 0.003 | 0.095 | 0.993 | 2.107 | 0.035 |
| depre_t1 ~~ d_depre_1 | -0.029 | -0.057 | 0.000 | -0.451 | -1.980 | 0.048 |
| d_depre_1 ~ sa07_11_t1 | 0.013 | -0.014 | 0.040 | 0.086 | 0.935 | 0.350 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.217 | 2.036 | 2.399 | 1.000 | 23.940 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 8666.308 | 8781.788 | 0.878 | 0.071 | 0.069 |
# parameters of interest
params_lcs_depre_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_depre_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.062 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.811 | NA | NA |
| d_depre_1 ~1 | 0.018 | -0.018 | 0.055 | 0.082 | 0.978 | 0.328 |
| depre_t1 ~1 | 4.409 | 4.345 | 4.474 | 14.905 | 133.496 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.051 | 0.003 | 0.098 | 0.993 | 2.090 | 0.037 |
| depre_t1 ~~ d_depre_1 | -0.031 | -0.061 | -0.001 | -0.464 | -2.028 | 0.043 |
| depre_t1 ~ frequ | 0.026 | -0.036 | 0.087 | 0.067 | 0.825 | 0.409 |
| d_depre_1 ~ frequ | 0.024 | -0.033 | 0.081 | 0.082 | 0.838 | 0.402 |
| frequ ~~ frequ | 0.583 | 0.392 | 0.774 | 1.000 | 5.984 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7993.789 | 8082.323 | 0.953 | 0.039 | 0.046 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", "d_volat_1 ~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.911 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.668 | NA | NA |
| d_volat_1 ~1 | 0.086 | -0.054 | 0.227 | 0.273 | 1.207 | 0.227 |
| volat_t1 ~1 | 1.662 | 1.509 | 1.815 | 3.849 | 21.272 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.099 | 0.021 | 0.177 | 0.990 | 2.491 | 0.013 |
| volat_t1 ~~ d_volat_1 | -0.030 | -0.083 | 0.022 | -0.223 | -1.132 | 0.258 |
| d_volat_1 ~ sa07_12_t1 | -0.024 | -0.073 | 0.025 | -0.098 | -0.967 | 0.334 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.682 | 1.503 | 1.861 | 1.000 | 18.422 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 9710.113 | 9825.592 | 0.983 | 0.025 | 0.046 |
# parameters of interest
params_lcs_volat_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_volat_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.913 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.667 | NA | NA |
| d_volat_1 ~1 | 0.020 | -0.039 | 0.079 | 0.067 | 0.667 | 0.505 |
| volat_t1 ~1 | 1.704 | 1.624 | 1.785 | 4.124 | 41.364 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.090 | 0.019 | 0.160 | 0.983 | 2.492 | 0.013 |
| volat_t1 ~~ d_volat_1 | -0.032 | -0.080 | 0.017 | -0.264 | -1.291 | 0.197 |
| volat_t1 ~ frequ | -0.115 | -0.203 | -0.026 | -0.210 | -2.539 | 0.011 |
| d_volat_1 ~ frequ | -0.053 | -0.140 | 0.035 | -0.132 | -1.179 | 0.238 |
| frequ ~~ frequ | 0.571 | 0.384 | 0.758 | 1.000 | 5.990 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8453.219 | 8541.754 | 0.986 | 0.021 | 0.041 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", "d_curio_1 ~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.045 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.686 | NA | NA |
| d_curio_1 ~1 | 0.033 | -0.057 | 0.122 | 0.165 | 0.721 | 0.471 |
| curio_t1 ~1 | 4.572 | 4.470 | 4.675 | 15.070 | 87.448 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.039 | -0.006 | 0.085 | 0.997 | 1.696 | 0.090 |
| curio_t1 ~~ d_curio_1 | -0.024 | -0.049 | 0.001 | -0.407 | -1.898 | 0.058 |
| d_curio_1 ~ sa07_13_t1 | -0.009 | -0.045 | 0.028 | -0.059 | -0.464 | 0.643 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.817 | 1.605 | 2.030 | 1.000 | 16.764 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10156.61 | 10272.09 | 0.986 | 0.022 | 0.043 |
# parameters of interest
params_lcs_curio_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_curio_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.043 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.685 | NA | NA |
| d_curio_1 ~1 | 0.013 | -0.030 | 0.056 | 0.065 | 0.602 | 0.547 |
| curio_t1 ~1 | 4.494 | 4.427 | 4.561 | 14.718 | 130.915 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.040 | -0.006 | 0.086 | 0.995 | 1.697 | 0.090 |
| curio_t1 ~~ d_curio_1 | -0.024 | -0.050 | 0.001 | -0.405 | -1.863 | 0.063 |
| curio_t1 ~ frequ | 0.057 | -0.017 | 0.132 | 0.145 | 1.503 | 0.133 |
| d_curio_1 ~ frequ | 0.018 | -0.057 | 0.092 | 0.067 | 0.462 | 0.644 |
| frequ ~~ frequ | 0.592 | 0.401 | 0.782 | 1.000 | 6.086 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8536.79 | 8625.324 | 0.986 | 0.032 | 0.041 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", "d_aesth_1 ~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.948 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.670 | NA | NA |
| d_aesth_1 ~1 | 0.000 | -0.007 | 0.008 | 0.015 | 0.094 | 0.925 |
| aesth_t1 ~1 | 3.206 | 3.077 | 3.336 | 90.898 | 48.618 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.999 | 4.204 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | -0.001 | 0.000 | -0.273 | -1.781 | 0.075 |
| d_aesth_1 ~ sa07_14_t1 | -0.001 | -0.003 | 0.002 | -0.033 | -0.414 | 0.679 |
| sa07_14_t1 ~~ sa07_14_t1 | 1.920 | 1.736 | 2.104 | 1.000 | 20.436 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 10206.37 | 10321.85 | 0.981 | 0.035 | 0.046 |
# parameters of interest
params_lcs_aesth_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_aesth_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.952 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.672 | NA | NA |
| d_aesth_1 ~1 | -0.001 | -0.005 | 0.003 | -0.044 | -0.574 | 0.566 |
| aesth_t1 ~1 | 3.208 | 3.079 | 3.338 | 92.276 | 48.628 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.985 | 4.195 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | -0.001 | 0.000 | -0.308 | -1.943 | 0.052 |
| aesth_t1 ~ frequ | 0.008 | 0.002 | 0.014 | 0.177 | 2.643 | 0.008 |
| d_aesth_1 ~ frequ | 0.004 | -0.002 | 0.010 | 0.122 | 1.356 | 0.175 |
| frequ ~~ frequ | 0.571 | 0.385 | 0.757 | 1.000 | 6.020 | 0.000 |
Results summary (*sa07_$$_t1* = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7902.874 | 7991.408 | 0.932 | 0.048 | 0.055 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", "d_imagi_1 ~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.875 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.550 | NA | NA |
| d_imagi_1 ~1 | -0.082 | -0.173 | 0.010 | -0.398 | -1.754 | 0.079 |
| imagi_t1 ~1 | 4.672 | 4.574 | 4.770 | 14.298 | 93.437 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.042 | 0.000 | 0.084 | 0.990 | 1.963 | 0.050 |
| imagi_t1 ~~ d_imagi_1 | -0.004 | -0.033 | 0.025 | -0.061 | -0.272 | 0.786 |
| d_imagi_1 ~ sa07_15_t1 | 0.014 | -0.017 | 0.045 | 0.098 | 0.884 | 0.377 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.081 | 1.900 | 2.263 | 1.000 | 22.520 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 30 | 9553.944 | 9669.424 | 0.947 | 0.044 | 0.052 |
# parameters of interest
params_lcs_imagi_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_imagi_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.873 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.554 | NA | NA |
| d_imagi_1 ~1 | -0.044 | -0.093 | 0.005 | -0.210 | -1.778 | 0.075 |
| imagi_t1 ~1 | 4.604 | 4.544 | 4.664 | 13.829 | 150.953 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.045 | 0.001 | 0.088 | 0.999 | 1.993 | 0.046 |
| imagi_t1 ~~ d_imagi_1 | -0.005 | -0.037 | 0.026 | -0.077 | -0.327 | 0.743 |
| imagi_t1 ~ frequ | 0.084 | 0.002 | 0.166 | 0.190 | 1.997 | 0.046 |
| d_imagi_1 ~ frequ | 0.006 | -0.068 | 0.081 | 0.023 | 0.171 | 0.864 |
| frequ ~~ frequ | 0.572 | 0.382 | 0.762 | 1.000 | 5.910 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
# main effects (change score)
df_table_hyp7_main <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~1 "),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~1 "),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~1 "),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~1 ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_main = estimate, std_main = std.all, p_main = p.value)
# moderation effects
df_table_hyp7_mod <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_mod = estimate, std_mod = std.all, p_mod = p.value)
# combine
df_table_hyp7 <- df_table_hyp7_main %>% left_join(df_table_hyp7_mod)Results summary across the Big Five traits: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7[1:10, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| extraversion | goals | 1.513 | 3.831 | 0.000 | -0.037 | -0.087 | 0.380 |
| extraversion | frequency | 1.579 | 4.107 | 0.000 | 0.056 | 0.110 | 0.153 |
| agreeableness | goals | 0.793 | 2.485 | 0.000 | 0.022 | 0.050 | 0.598 |
| agreeableness | frequency | 0.999 | 3.188 | 0.000 | 0.090 | 0.218 | 0.012 |
| conscientiousness | goals | 1.323 | 3.809 | 0.003 | -0.020 | -0.065 | 0.330 |
| conscientiousness | frequency | 1.465 | 4.200 | 0.001 | 0.088 | 0.189 | 0.030 |
| neuroticism | goals | 0.469 | 1.449 | 0.000 | 0.056 | 0.139 | 0.119 |
| neuroticism | frequency | 0.493 | 1.504 | 0.000 | -0.043 | -0.101 | 0.190 |
| openness | goals | 0.674 | 2.854 | 0.010 | 0.007 | 0.021 | 0.811 |
| openness | frequency | 0.738 | 3.116 | 0.005 | 0.037 | 0.119 | 0.194 |
Results summary across the Big Five facets: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7[11:40, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| sociability | goals | 1.235 | 4.845 | 0.001 | -0.008 | -0.045 | 0.648 |
| sociability | frequency | 1.242 | 4.934 | 0.001 | -0.030 | -0.090 | 0.350 |
| assertiveness | goals | 1.167 | 5.727 | 0.003 | -0.014 | -0.098 | 0.261 |
| assertiveness | frequency | 1.313 | 6.473 | 0.001 | 0.074 | 0.272 | 0.015 |
| energy | goals | 0.935 | 3.950 | 0.004 | 0.025 | 0.146 | 0.112 |
| energy | frequency | 1.146 | 5.041 | 0.002 | -0.059 | -0.198 | 0.137 |
| compassion | goals | 0.883 | 2.637 | 0.033 | -0.051 | -0.198 | 0.058 |
| compassion | frequency | 0.666 | 1.954 | 0.145 | -0.030 | -0.067 | 0.673 |
| respectfulness | goals | 0.889 | 3.744 | 0.087 | 0.003 | 0.019 | 0.852 |
| respectfulness | frequency | 1.231 | 5.155 | 0.018 | 0.094 | 0.297 | 0.012 |
| trust | goals | 0.369 | 1.307 | 0.029 | 0.010 | 0.050 | 0.600 |
| trust | frequency | 0.470 | 1.614 | 0.005 | -0.070 | -0.183 | 0.120 |
| organization | goals | 0.593 | 2.811 | 0.000 | 0.003 | 0.023 | 0.745 |
| organization | frequency | 0.738 | 3.758 | 0.000 | -0.084 | -0.318 | 0.005 |
| productiveness | goals | 0.467 | 1.726 | 0.012 | 0.001 | 0.007 | 0.924 |
| productiveness | frequency | 0.516 | 1.917 | 0.008 | -0.090 | -0.254 | 0.018 |
| responsibility | goals | 1.497 | 3.991 | 0.002 | -0.017 | -0.064 | 0.424 |
| responsibility | frequency | 1.571 | 4.167 | 0.001 | 0.073 | 0.146 | 0.126 |
| anxiety | goals | 0.949 | 3.719 | 0.318 | 0.003 | 0.018 | 0.885 |
| anxiety | frequency | 0.897 | 3.662 | 0.383 | 0.006 | 0.020 | 0.892 |
| depression | goals | 1.528 | 6.872 | 0.010 | 0.005 | 0.031 | 0.704 |
| depression | frequency | 1.579 | 6.987 | 0.006 | 0.033 | 0.113 | 0.206 |
| volatility | goals | 0.358 | 1.130 | 0.080 | -0.021 | -0.088 | 0.349 |
| volatility | frequency | 0.353 | 1.170 | 0.074 | -0.075 | -0.188 | 0.101 |
| curiosity | goals | 1.265 | 6.356 | 0.003 | -0.018 | -0.122 | 0.276 |
| curiosity | frequency | 1.217 | 6.071 | 0.006 | 0.033 | 0.126 | 0.326 |
| aesthetic | goals | 0.619 | 24.722 | 0.045 | 0.000 | -0.025 | 0.737 |
| aesthetic | frequency | 0.703 | 28.623 | 0.029 | 0.006 | 0.177 | 0.062 |
| imagination | goals | 0.098 | 0.478 | 0.880 | 0.013 | 0.091 | 0.400 |
| imagination | frequency | 0.184 | 0.874 | 0.787 | 0.011 | 0.038 | 0.795 |
Prepare data frame for plotting:
df_table_hyp7_plot <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp7_plot <- df_table_hyp7_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("ideal", levels = c("ideal"), labels = c("ideal"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp7_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
We will explore a) whether change in psychological well-being indicators as well as the difference between real- and ideal- self will differ across groups at follow-up.
Life satisfaction: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_swls_group_config <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2a", "lamb2b")*sw06_02_t1 + c("lamb3a", "lamb3b")*sw06_03_t1 + c("lamb4a", "lamb4b")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2a", "lamb2b")*sw06_02_t2 + c("lamb3a", "lamb3b")*sw06_03_t2 + c("lamb4a", "lamb4b")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score remains unconditional
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_config <- sem(mi_lcs_swls_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_swls_group_weak <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_weak <- sem(mi_lcs_swls_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_swls_group_strong <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_strong <- sem(mi_lcs_swls_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Life satisfaction: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_swls_group_config, lcs_swls_group_weak, lcs_swls_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_swls_group_config 50 14267 14441 93.624
lcs_swls_group_weak 53 14269 14429 101.697 9.1169 3 0.02778 *
lcs_swls_group_strong 58 14264 14401 106.064 4.4012 5 0.49321
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# show model with varying latent change parameters
# -> key parameter is "d_swls_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_swls_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_swls_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_swls_1 ~1 | d_int_a | 0.853 | 1.297 | 6.040 | 0 |
| d_swls_1 ~1 | d_int_b | 0.669 | 1.004 | 4.835 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_swls_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_swls_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_swls_1 ~1 | d_int | 0.767 | 1.186 | 7.817 | 0 |
| d_swls_1 ~1 | d_int | 0.767 | 1.133 | 7.817 | 0 |
# whole model (weak invariance)
summary(lcs_swls_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 60 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 58
Number of equality constraints 23
Number of observations per group: Used Total
Self-Acceptance 347 347
Self-Improvement 367 368
Number of missing patterns per group:
Self-Acceptance 3
Self-Improvement 3
Model Test User Model:
Standard Scaled
Test Statistic 101.696 93.809
Degrees of freedom 53 53
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.084
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 54.733 50.488
Self-Improvement 46.963 43.321
Model Test Baseline Model:
Test statistic 3141.266 2318.950
Degrees of freedom 56 56
P-value 0.000 0.000
Scaling correction factor 1.355
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.984 0.982
Tucker-Lewis Index (TLI) 0.983 0.981
Robust Comparative Fit Index (CFI) 0.986
Robust Tucker-Lewis Index (TLI) 0.985
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -7099.624 -7099.624
Scaling correction factor 0.721
for the MLR correction
Loglikelihood unrestricted model (H1) -7048.776 -7048.776
Scaling correction factor 1.128
for the MLR correction
Akaike (AIC) 14269.248 14269.248
Bayesian (BIC) 14429.229 14429.229
Sample-size adjusted Bayesian (SABIC) 14318.095 14318.095
Root Mean Square Error of Approximation:
RMSEA 0.051 0.046
90 Percent confidence interval - lower 0.036 0.031
90 Percent confidence interval - upper 0.066 0.061
P-value H_0: RMSEA <= 0.050 0.448 0.637
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.049
90 Percent confidence interval - lower 0.032
90 Percent confidence interval - upper 0.066
P-value H_0: Robust RMSEA <= 0.050 0.500
P-value H_0: Robust RMSEA >= 0.080 0.001
Standardized Root Mean Square Residual:
SRMR 0.045 0.045
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t1 =~
s06_01_ 1.000 0.968 0.811
s06_02_ (lmb2) 0.832 0.031 27.204 0.000 0.805 0.727
s06_03_ (lmb3) 1.038 0.033 31.249 0.000 1.004 0.833
s06_04_ (lmb4) 0.857 0.034 25.098 0.000 0.829 0.697
swls_t2 =~
s06_01_ 1.000 0.995 0.819
s06_02_ (lmb2) 0.832 0.031 27.204 0.000 0.828 0.737
s06_03_ (lmb3) 1.038 0.033 31.249 0.000 1.033 0.840
s06_04_ (lmb4) 0.857 0.034 25.098 0.000 0.853 0.707
d_swls_1 =~
swls_t2 1.000 0.661 0.661
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t2 ~
swls_t1 1.000 0.972 0.972
d_swls_1 ~
swls_t1 (fb_a) -0.202 0.046 -4.426 0.000 -0.297 -0.297
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.sw06_01_t1 ~~
.s06_01_ (cov1) 0.106 0.055 1.924 0.054 0.106 0.218
.sw06_02_t1 ~~
.s06_02_ (cov2) 0.216 0.047 4.585 0.000 0.216 0.375
.sw06_03_t1 ~~
.s06_03_ (cov3) 0.128 0.042 3.070 0.002 0.128 0.288
.sw06_04_t1 ~~
.s06_04_ (cov4) 0.272 0.051 5.316 0.000 0.272 0.375
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_n_) 0.853 0.141 6.040 0.000 1.297 1.297
swls_t1 (wb__) 2.682 0.060 44.414 0.000 2.771 2.771
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2a) 0.578 0.098 5.881 0.000 0.578 0.523
.s06_03_ (m3a) -0.038 0.109 -0.354 0.724 -0.038 -0.032
.s06_04_ (m4a) 0.418 0.109 3.837 0.000 0.418 0.352
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2a) 0.578 0.098 5.881 0.000 0.578 0.515
.s06_03_ (m3a) -0.038 0.109 -0.354 0.724 -0.038 -0.031
.s06_04_ (m4a) 0.418 0.109 3.837 0.000 0.418 0.347
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_v_) 0.394 0.058 6.781 0.000 0.912 0.912
swls_t1 (wb__) 0.937 0.066 14.167 0.000 1.000 1.000
.s06_01_ (res1) 0.486 0.060 8.077 0.000 0.486 0.342
.s06_02_ (res2) 0.577 0.045 12.796 0.000 0.577 0.471
.s06_03_ (res3) 0.444 0.048 9.269 0.000 0.444 0.305
.s06_04_ (res4) 0.727 0.051 14.283 0.000 0.727 0.514
.s06_01_ (res1) 0.486 0.060 8.077 0.000 0.486 0.329
.s06_02_ (res2) 0.577 0.045 12.796 0.000 0.577 0.457
.s06_03_ (res3) 0.444 0.048 9.269 0.000 0.444 0.294
.s06_04_ (res4) 0.727 0.051 14.283 0.000 0.727 0.500
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t1 =~
s06_01_ 1.000 0.917 0.820
s06_02_ (lmb2) 0.832 0.031 27.204 0.000 0.763 0.690
s06_03_ (lmb3) 1.038 0.033 31.249 0.000 0.952 0.828
s06_04_ (lmb4) 0.857 0.034 25.098 0.000 0.786 0.657
swls_t2 =~
s06_01_ 1.000 1.019 0.847
s06_02_ (lmb2) 0.832 0.031 27.204 0.000 0.848 0.727
s06_03_ (lmb3) 1.038 0.033 31.249 0.000 1.058 0.854
s06_04_ (lmb4) 0.857 0.034 25.098 0.000 0.874 0.695
d_swls_1 =~
swls_t2 1.000 0.654 0.654
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t2 ~
swls_t1 1.000 0.900 0.900
d_swls_1 ~
swls_t1 (fb_b) -0.146 0.045 -3.261 0.001 -0.202 -0.202
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.sw06_01_t1 ~~
.s06_01_ (cv1b) 0.059 0.043 1.374 0.169 0.059 0.145
.sw06_02_t1 ~~
.s06_02_ (cv2b) 0.367 0.049 7.436 0.000 0.367 0.573
.sw06_03_t1 ~~
.s06_03_ (cv3b) 0.050 0.040 1.252 0.211 0.050 0.120
.sw06_04_t1 ~~
.s06_04_ (cv4b) 0.320 0.064 4.979 0.000 0.320 0.392
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_n_) 0.669 0.138 4.835 0.000 1.004 1.004
swls_t1 (wb__) 2.705 0.058 46.977 0.000 2.949 2.949
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2b) 0.570 0.102 5.612 0.000 0.570 0.515
.s06_03_ (m3b) -0.006 0.105 -0.056 0.956 -0.006 -0.005
.s06_04_ (m4b) 0.526 0.109 4.801 0.000 0.526 0.439
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2b) 0.570 0.102 5.612 0.000 0.570 0.489
.s06_03_ (m3b) -0.006 0.105 -0.056 0.956 -0.006 -0.005
.s06_04_ (m4b) 0.526 0.109 4.801 0.000 0.526 0.418
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_v_) 0.426 0.053 7.987 0.000 0.959 0.959
swls_t1 (wb__) 0.841 0.070 11.982 0.000 1.000 1.000
.s06_01_ (rs1b) 0.409 0.045 9.101 0.000 0.409 0.327
.s06_02_ (rs2b) 0.641 0.050 12.911 0.000 0.641 0.524
.s06_03_ (rs3b) 0.416 0.039 10.526 0.000 0.416 0.314
.s06_04_ (rs4b) 0.816 0.062 13.191 0.000 0.816 0.569
.s06_01_ (rs1b) 0.409 0.045 9.101 0.000 0.409 0.283
.s06_02_ (rs2b) 0.641 0.050 12.911 0.000 0.641 0.471
.s06_03_ (rs3b) 0.416 0.039 10.526 0.000 0.416 0.271
.s06_04_ (rs4b) 0.816 0.062 13.191 0.000 0.816 0.517Meaning in life: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_meaning_group_config <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2a", "lamb2b")*meaning_par2_t1 + c("lamb3a", "lamb3b")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2a", "lamb2b")*meaning_par2_t2 + c("lamb3a", "lamb3b")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_config <- sem(mi_lcs_meaning_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_meaning_group_weak <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_weak <- sem(mi_lcs_meaning_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_meaning_group_strong <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_strong <- sem(mi_lcs_meaning_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_meaning_group_config, lcs_meaning_group_weak, lcs_meaning_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_meaning_group_config 24 10472 10609 38.067
lcs_meaning_group_weak 26 10470 10598 40.184 1.9319 2 0.3806
lcs_meaning_group_strong 30 10466 10575 44.216 3.7939 4 0.4346# show model with varying latent change parameters
# -> key parameter is "d_meaning_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_meaning_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_meaning_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_meaning_1 ~1 | d_int_a | 1.289 | 1.595 | 6.828 | 0 |
| d_meaning_1 ~1 | d_int_b | 1.479 | 1.792 | 7.535 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_meaning_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_meaning_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_meaning_1 ~1 | d_int | 1.384 | 1.682 | 10.052 | 0 |
| d_meaning_1 ~1 | d_int | 1.384 | 1.701 | 10.052 | 0 |
# whole model (weak invariance)
summary(lcs_meaning_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 68 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 347 347
Self-Improvement 367 368
Number of missing patterns per group:
Self-Acceptance 3
Self-Improvement 3
Model Test User Model:
Standard Scaled
Test Statistic 40.184 37.276
Degrees of freedom 26 26
P-value (Chi-square) 0.037 0.071
Scaling correction factor 1.078
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 19.692 18.267
Self-Improvement 20.492 19.009
Model Test Baseline Model:
Test statistic 2964.407 2380.993
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.245
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.995 0.995
Tucker-Lewis Index (TLI) 0.994 0.994
Robust Comparative Fit Index (CFI) 0.996
Robust Tucker-Lewis Index (TLI) 0.995
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -5206.794 -5206.794
Scaling correction factor 0.734
for the MLR correction
Loglikelihood unrestricted model (H1) -5186.702 -5186.702
Scaling correction factor 1.117
for the MLR correction
Akaike (AIC) 10469.587 10469.587
Bayesian (BIC) 10597.572 10597.572
Sample-size adjusted Bayesian (SABIC) 10508.665 10508.665
Root Mean Square Error of Approximation:
RMSEA 0.039 0.035
90 Percent confidence interval - lower 0.010 0.000
90 Percent confidence interval - upper 0.062 0.058
P-value H_0: RMSEA <= 0.050 0.764 0.851
P-value H_0: RMSEA >= 0.080 0.001 0.000
Robust RMSEA 0.038
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.063
P-value H_0: Robust RMSEA <= 0.050 0.761
P-value H_0: Robust RMSEA >= 0.080 0.002
Standardized Root Mean Square Residual:
SRMR 0.032 0.032
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t1 =~
mnn_1_1 1.000 0.949 0.793
mnn_2_1 (lmb2) 1.257 0.039 31.903 0.000 1.193 0.928
mnn_3_1 (lmb3) 1.117 0.035 31.759 0.000 1.060 0.854
meaning_t2 =~
mnn_1_2 1.000 0.994 0.806
mnn_2_2 (lmb2) 1.257 0.039 31.903 0.000 1.249 0.933
mnn_3_2 (lmb3) 1.117 0.035 31.759 0.000 1.110 0.864
d_meaning_1 =~
mnng_t2 1.000 0.813 0.813
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t2 ~
mnng_t1 1.000 0.955 0.955
d_meaning_1 ~
mnng_t1 (fb_a) -0.314 0.057 -5.539 0.000 -0.369 -0.369
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.meaning_par1_t1 ~~
.mnn_1_2 (cov1) 0.292 0.042 7.008 0.000 0.292 0.548
.meaning_par2_t1 ~~
.mnn_2_2 (cov2) -0.083 0.034 -2.417 0.016 -0.083 -0.361
.meaning_par3_t1 ~~
.mnn_3_2 (cov3) 0.152 0.032 4.703 0.000 0.152 0.364
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_n_) 1.289 0.189 6.828 0.000 1.595 1.595
mnng_t1 (wb__) 3.290 0.061 53.595 0.000 3.466 3.466
.mnn_1_1 0.000 0.000 0.000
.mnn_2_1 (m2a) -0.260 0.143 -1.812 0.070 -0.260 -0.202
.mnn_3_1 (m3a) -0.046 0.129 -0.353 0.724 -0.046 -0.037
.mnn_1_2 0.000 0.000 0.000
.mnn_2_2 (m2a) -0.260 0.143 -1.812 0.070 -0.260 -0.194
.mnn_3_2 (m3a) -0.046 0.129 -0.353 0.724 -0.046 -0.035
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_v_) 0.564 0.073 7.756 0.000 0.864 0.864
mnng_t1 (wb__) 0.901 0.083 10.800 0.000 1.000 1.000
.mnn_1_1 (res1) 0.533 0.043 12.485 0.000 0.533 0.372
.mnn_2_1 (res2) 0.230 0.035 6.535 0.000 0.230 0.139
.mnn_3_1 (res3) 0.419 0.039 10.793 0.000 0.419 0.271
.mnn_1_2 (res1) 0.533 0.043 12.485 0.000 0.533 0.350
.mnn_2_2 (res2) 0.230 0.035 6.535 0.000 0.230 0.129
.mnn_3_2 (res3) 0.419 0.039 10.793 0.000 0.419 0.254
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t1 =~
mnn_1_1 1.000 0.900 0.770
mnn_2_1 (lmb2) 1.257 0.039 31.903 0.000 1.131 0.925
mnn_3_1 (lmb3) 1.117 0.035 31.759 0.000 1.005 0.826
meaning_t2 =~
mnn_1_2 1.000 0.940 0.783
mnn_2_2 (lmb2) 1.257 0.039 31.903 0.000 1.182 0.931
mnn_3_2 (lmb3) 1.117 0.035 31.759 0.000 1.050 0.838
d_meaning_1 =~
mnng_t2 1.000 0.877 0.877
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t2 ~
mnng_t1 1.000 0.957 0.957
d_meaning_1 ~
mnng_t1 (fb_b) -0.374 0.056 -6.719 0.000 -0.408 -0.408
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.meaning_par1_t1 ~~
.mnn_1_2 (cv1b) 0.342 0.048 7.152 0.000 0.342 0.615
.meaning_par2_t1 ~~
.mnn_2_2 (cv2b) -0.036 0.035 -1.037 0.300 -0.036 -0.168
.meaning_par3_t1 ~~
.mnn_3_2 (cv3b) 0.201 0.039 5.129 0.000 0.201 0.429
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_n_) 1.479 0.196 7.535 0.000 1.792 1.792
mnng_t1 (wb__) 3.146 0.059 53.754 0.000 3.495 3.495
.mnn_1_1 0.000 0.000 0.000
.mnn_2_1 (m2b) -0.161 0.138 -1.170 0.242 -0.161 -0.132
.mnn_3_1 (m3b) 0.028 0.119 0.234 0.815 0.028 0.023
.mnn_1_2 0.000 0.000 0.000
.mnn_2_2 (m2b) -0.161 0.138 -1.170 0.242 -0.161 -0.127
.mnn_3_2 (m3b) 0.028 0.119 0.234 0.815 0.028 0.022
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_v_) 0.567 0.077 7.373 0.000 0.833 0.833
mnng_t1 (wb__) 0.810 0.081 10.015 0.000 1.000 1.000
.mnn_1_1 (rs1b) 0.556 0.048 11.598 0.000 0.556 0.407
.mnn_2_1 (rs2b) 0.214 0.040 5.378 0.000 0.214 0.144
.mnn_3_1 (rs3b) 0.470 0.044 10.739 0.000 0.470 0.317
.mnn_1_2 (rs1b) 0.556 0.048 11.598 0.000 0.556 0.386
.mnn_2_2 (rs2b) 0.214 0.040 5.378 0.000 0.214 0.133
.mnn_3_2 (rs3b) 0.470 0.044 10.739 0.000 0.470 0.299Self-esteem: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_selfes_group_config <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2a", "lamb2b")*selfes_par2_t1 + c("lamb3a", "lamb3b")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2a", "lamb2b")*selfes_par2_t2 + c("lamb3a", "lamb3b")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_config <- sem(mi_lcs_selfes_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_selfes_group_weak <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_weak <- sem(mi_lcs_selfes_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_selfes_group_strong <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_strong <- sem(mi_lcs_selfes_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_selfes_group_config, lcs_selfes_group_weak, lcs_selfes_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_selfes_group_config 24 7347.4 7484.5 20.707
lcs_selfes_group_weak 26 7345.5 7473.4 22.790 2.0845 2 0.3527
lcs_selfes_group_strong 30 7344.2 7453.9 29.500 6.4175 4 0.1701# show model with varying latent change parameters
# -> key parameter is "d_selfes_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_selfes_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_selfes_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_selfes_1 ~1 | d_int_a | 1.178 | 2.061 | 7.605 | 0 |
| d_selfes_1 ~1 | d_int_b | 0.965 | 1.846 | 6.713 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_selfes_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_selfes_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_selfes_1 ~1 | d_int | 1.077 | 1.916 | 10.19 | 0 |
| d_selfes_1 ~1 | d_int | 1.077 | 2.020 | 10.19 | 0 |
# whole model (weak invariance)
summary(lcs_selfes_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 75 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 347 347
Self-Improvement 367 368
Number of missing patterns per group:
Self-Acceptance 3
Self-Improvement 3
Model Test User Model:
Standard Scaled
Test Statistic 22.790 21.782
Degrees of freedom 26 26
P-value (Chi-square) 0.645 0.701
Scaling correction factor 1.046
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 8.879 8.486
Self-Improvement 13.911 13.296
Model Test Baseline Model:
Test statistic 3753.491 3014.202
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.245
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.001 1.002
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.002
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3644.731 -3644.731
Scaling correction factor 0.685
for the MLR correction
Loglikelihood unrestricted model (H1) -3633.336 -3633.336
Scaling correction factor 1.062
for the MLR correction
Akaike (AIC) 7345.462 7345.462
Bayesian (BIC) 7473.447 7473.447
Sample-size adjusted Bayesian (SABIC) 7384.540 7384.540
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.035 0.032
P-value H_0: RMSEA <= 0.050 0.996 0.998
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.034
P-value H_0: Robust RMSEA <= 0.050 0.995
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.017 0.017
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t1 =~
slf_1_1 1.000 0.792 0.880
slf_2_1 (lmb2) 1.148 0.026 44.540 0.000 0.909 0.917
slf_3_1 (lmb3) 1.160 0.027 42.206 0.000 0.919 0.918
selfes_t2 =~
slf_1_2 1.000 0.776 0.876
slf_2_2 (lmb2) 1.148 0.026 44.540 0.000 0.891 0.914
slf_3_2 (lmb3) 1.160 0.027 42.206 0.000 0.901 0.915
d_selfes_1 =~
slfs_t2 1.000 0.737 0.737
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t2 ~
slfs_t1 1.000 1.020 1.020
d_selfes_1 ~
slfs_t1 (fb_a) -0.280 0.044 -6.340 0.000 -0.388 -0.388
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.selfes_par1_t1 ~~
.slf_1_2 (cov1) 0.076 0.014 5.571 0.000 0.076 0.414
.selfes_par2_t1 ~~
.slf_2_2 (cov2) 0.059 0.014 4.105 0.000 0.059 0.375
.selfes_par3_t1 ~~
.slf_3_2 (cov3) 0.036 0.015 2.456 0.014 0.036 0.229
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_n_) 1.178 0.155 7.605 0.000 2.061 2.061
slfs_t1 (wb__) 3.129 0.048 65.182 0.000 3.953 3.953
.slf_1_1 0.000 0.000 0.000
.slf_2_1 (m2a) -0.475 0.088 -5.411 0.000 -0.475 -0.479
.slf_3_1 (m3a) -0.449 0.090 -4.997 0.000 -0.449 -0.449
.slf_1_2 0.000 0.000 0.000
.slf_2_2 (m2a) -0.475 0.088 -5.411 0.000 -0.475 -0.487
.slf_3_2 (m3a) -0.449 0.090 -4.997 0.000 -0.449 -0.456
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_v_) 0.278 0.033 8.512 0.000 0.849 0.849
slfs_t1 (wb__) 0.627 0.046 13.582 0.000 1.000 1.000
.slf_1_1 (res1) 0.182 0.015 12.431 0.000 0.182 0.226
.slf_2_1 (res2) 0.157 0.018 8.958 0.000 0.157 0.160
.slf_3_1 (res3) 0.157 0.016 9.664 0.000 0.157 0.157
.slf_1_2 (res1) 0.182 0.015 12.431 0.000 0.182 0.232
.slf_2_2 (res2) 0.157 0.018 8.958 0.000 0.157 0.165
.slf_3_2 (res3) 0.157 0.016 9.664 0.000 0.157 0.162
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t1 =~
slf_1_1 1.000 0.721 0.852
slf_2_1 (lmb2) 1.148 0.026 44.540 0.000 0.827 0.913
slf_3_1 (lmb3) 1.160 0.027 42.206 0.000 0.836 0.887
selfes_t2 =~
slf_1_2 1.000 0.749 0.861
slf_2_2 (lmb2) 1.148 0.026 44.540 0.000 0.860 0.919
slf_3_2 (lmb3) 1.160 0.027 42.206 0.000 0.870 0.894
d_selfes_1 =~
slfs_t2 1.000 0.698 0.698
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t2 ~
slfs_t1 1.000 0.962 0.962
d_selfes_1 ~
slfs_t1 (fb_b) -0.223 0.042 -5.266 0.000 -0.307 -0.307
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.selfes_par1_t1 ~~
.slf_1_2 (cv1b) 0.082 0.016 5.204 0.000 0.082 0.416
.selfes_par2_t1 ~~
.slf_2_2 (cv2b) 0.013 0.013 0.983 0.325 0.013 0.096
.selfes_par3_t1 ~~
.slf_3_2 (cv3b) 0.051 0.018 2.819 0.005 0.051 0.271
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_n_) 0.965 0.144 6.713 0.000 1.846 1.846
slfs_t1 (wb__) 3.126 0.043 72.987 0.000 4.336 4.336
.slf_1_1 0.000 0.000 0.000
.slf_2_1 (m2b) -0.412 0.087 -4.732 0.000 -0.412 -0.455
.slf_3_1 (m3b) -0.462 0.094 -4.892 0.000 -0.462 -0.489
.slf_1_2 0.000 0.000 0.000
.slf_2_2 (m2b) -0.412 0.087 -4.732 0.000 -0.412 -0.440
.slf_3_2 (m3b) -0.462 0.094 -4.892 0.000 -0.462 -0.474
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_v_) 0.248 0.029 8.457 0.000 0.906 0.906
slfs_t1 (wb__) 0.520 0.039 13.388 0.000 1.000 1.000
.slf_1_1 (rs1b) 0.196 0.017 11.861 0.000 0.196 0.274
.slf_2_1 (rs2b) 0.137 0.014 9.610 0.000 0.137 0.166
.slf_3_1 (rs3b) 0.190 0.018 10.780 0.000 0.190 0.214
.slf_1_2 (rs1b) 0.196 0.017 11.861 0.000 0.196 0.259
.slf_2_2 (rs2b) 0.137 0.014 9.610 0.000 0.137 0.156
.slf_3_2 (rs3b) 0.190 0.018 10.780 0.000 0.190 0.201Self-concept clarity: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_concept_group_config <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2a", "lamb2b")*concept_par2_t1 + c("lamb3a", "lamb3b")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2a", "lamb2b")*concept_par2_t2 + c("lamb3a", "lamb3b")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_config <- sem(mi_lcs_concept_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_concept_group_weak <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_weak <- sem(mi_lcs_concept_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_concept_group_strong <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_strong <- sem(mi_lcs_concept_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_concept_group_config, lcs_concept_group_weak, lcs_concept_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_concept_group_config 24 8376.4 8513.5 32.823
lcs_concept_group_weak 26 8373.0 8500.9 33.355 0.46999 2 0.7906
lcs_concept_group_strong 30 8365.7 8475.4 34.073 0.72313 4 0.9484# show model with varying latent change parameters
# -> key parameter is "d_concept_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_concept_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_concept_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_concept_1 ~1 | d_int_a | 1.234 | 2.125 | 7.949 | 0 |
| d_concept_1 ~1 | d_int_b | 1.077 | 1.986 | 7.104 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_concept_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_concept_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_concept_1 ~1 | d_int | 1.159 | 2.024 | 10.612 | 0 |
| d_concept_1 ~1 | d_int | 1.159 | 2.108 | 10.612 | 0 |
# whole model (weak invariance)
summary(lcs_concept_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 64 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 347 347
Self-Improvement 367 368
Number of missing patterns per group:
Self-Acceptance 3
Self-Improvement 3
Model Test User Model:
Standard Scaled
Test Statistic 33.355 32.673
Degrees of freedom 26 26
P-value (Chi-square) 0.152 0.172
Scaling correction factor 1.021
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 24.853 24.344
Self-Improvement 8.503 8.329
Model Test Baseline Model:
Test statistic 2699.514 2215.007
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.219
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997 0.997
Tucker-Lewis Index (TLI) 0.997 0.996
Robust Comparative Fit Index (CFI) 0.997
Robust Tucker-Lewis Index (TLI) 0.997
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4158.476 -4158.476
Scaling correction factor 0.691
for the MLR correction
Loglikelihood unrestricted model (H1) -4141.799 -4141.799
Scaling correction factor 1.054
for the MLR correction
Akaike (AIC) 8372.952 8372.952
Bayesian (BIC) 8500.937 8500.937
Sample-size adjusted Bayesian (SABIC) 8412.030 8412.030
Root Mean Square Error of Approximation:
RMSEA 0.028 0.027
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.053 0.052
P-value H_0: RMSEA <= 0.050 0.918 0.931
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.028
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.055
P-value H_0: Robust RMSEA <= 0.050 0.904
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.026 0.026
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t1 =~
cnc_1_1 1.000 0.679 0.813
cnc_2_1 (lmb2) 1.241 0.043 28.864 0.000 0.843 0.857
cnc_3_1 (lmb3) 1.316 0.040 32.583 0.000 0.894 0.879
concept_t2 =~
cnc_1_2 1.000 0.682 0.814
cnc_2_2 (lmb2) 1.241 0.043 28.864 0.000 0.847 0.858
cnc_3_2 (lmb3) 1.316 0.040 32.583 0.000 0.898 0.880
d_concept_1 =~
cncpt_2 1.000 0.851 0.851
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t2 ~
cncpt_1 1.000 0.995 0.995
d_concept_1 ~
cncpt_1 (fb_a) -0.361 0.051 -7.073 0.000 -0.422 -0.422
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.concept_par1_t1 ~~
.cnc_1_2 (cov1) 0.032 0.019 1.656 0.098 0.032 0.135
.concept_par2_t1 ~~
.cnc_2_2 (cov2) 0.086 0.024 3.565 0.000 0.086 0.335
.concept_par3_t1 ~~
.cnc_3_2 (cov3) 0.084 0.025 3.399 0.001 0.084 0.357
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_n_) 1.234 0.155 7.949 0.000 2.125 2.125
cncpt_1 (wb__) 2.919 0.043 68.408 0.000 4.297 4.297
.cnc_1_1 0.000 0.000 0.000
.cnc_2_1 (m2a) -0.493 0.136 -3.629 0.000 -0.493 -0.501
.cnc_3_1 (m3a) -1.037 0.126 -8.237 0.000 -1.037 -1.020
.cnc_1_2 0.000 0.000 0.000
.cnc_2_2 (m2a) -0.493 0.136 -3.629 0.000 -0.493 -0.499
.cnc_3_2 (m3a) -1.037 0.126 -8.237 0.000 -1.037 -1.016
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_v_) 0.277 0.033 8.311 0.000 0.822 0.822
cncpt_1 (wb__) 0.461 0.038 12.099 0.000 1.000 1.000
.cnc_1_1 (res1) 0.237 0.019 12.605 0.000 0.237 0.339
.cnc_2_1 (res2) 0.258 0.025 10.267 0.000 0.258 0.266
.cnc_3_1 (res3) 0.235 0.025 9.570 0.000 0.235 0.227
.cnc_1_2 (res1) 0.237 0.019 12.605 0.000 0.237 0.337
.cnc_2_2 (res2) 0.258 0.025 10.267 0.000 0.258 0.264
.cnc_3_2 (res3) 0.235 0.025 9.570 0.000 0.235 0.226
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t1 =~
cnc_1_1 1.000 0.627 0.785
cnc_2_1 (lmb2) 1.241 0.043 28.864 0.000 0.778 0.844
cnc_3_1 (lmb3) 1.316 0.040 32.583 0.000 0.825 0.868
concept_t2 =~
cnc_1_2 1.000 0.677 0.808
cnc_2_2 (lmb2) 1.241 0.043 28.864 0.000 0.841 0.862
cnc_3_2 (lmb3) 1.316 0.040 32.583 0.000 0.891 0.884
d_concept_1 =~
cncpt_2 1.000 0.801 0.801
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t2 ~
cncpt_1 1.000 0.926 0.926
d_concept_1 ~
cncpt_1 (fb_b) -0.290 0.049 -5.944 0.000 -0.336 -0.336
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.concept_par1_t1 ~~
.cnc_1_2 (cv1b) 0.098 0.019 5.048 0.000 0.098 0.399
.concept_par2_t1 ~~
.cnc_2_2 (cv2b) 0.056 0.024 2.330 0.020 0.056 0.228
.concept_par3_t1 ~~
.cnc_3_2 (cv3b) 0.059 0.024 2.460 0.014 0.059 0.266
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_n_) 1.077 0.152 7.104 0.000 1.986 1.986
cncpt_1 (wb__) 2.936 0.041 72.093 0.000 4.683 4.683
.cnc_1_1 0.000 0.000 0.000
.cnc_2_1 (m2b) -0.511 0.134 -3.804 0.000 -0.511 -0.555
.cnc_3_1 (m3b) -1.044 0.127 -8.222 0.000 -1.044 -1.099
.cnc_1_2 0.000 0.000 0.000
.cnc_2_2 (m2b) -0.511 0.134 -3.804 0.000 -0.511 -0.524
.cnc_3_2 (m3b) -1.044 0.127 -8.222 0.000 -1.044 -1.036
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_v_) 0.261 0.030 8.560 0.000 0.887 0.887
cncpt_1 (wb__) 0.393 0.036 11.046 0.000 1.000 1.000
.cnc_1_1 (rs1b) 0.245 0.018 13.471 0.000 0.245 0.384
.cnc_2_1 (rs2b) 0.244 0.023 10.659 0.000 0.244 0.287
.cnc_3_1 (rs3b) 0.222 0.024 9.208 0.000 0.222 0.246
.cnc_1_2 (rs1b) 0.245 0.018 13.471 0.000 0.245 0.348
.cnc_2_2 (rs2b) 0.244 0.023 10.659 0.000 0.244 0.257
.cnc_3_2 (rs3b) 0.222 0.024 9.208 0.000 0.222 0.219Profile correlations by group and measurement occasion (mixed effects models) - results:
df_sbsa <- df_sbsa %>% mutate(time_d = time - 1)
psych::describeBy(df_sbsa$profile_corr_item_z, list(df_sbsa$rando, df_sbsa$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 342 0.36 0.48 0.36 0.35 0.47 -1.05 2.25 3.3 0.39 0.99 0.03
------------------------------------------------------------
: Self-Improvement
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 362 0.38 0.45 0.36 0.35 0.44 -0.71 2.01 2.72 0.59 0.68 0.02
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 326 0.55 0.58 0.49 0.51 0.52 -0.9 3.32 4.22 1 3.31 0.03
------------------------------------------------------------
: Self-Improvement
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 338 0.56 0.48 0.53 0.55 0.48 -0.77 2.27 3.04 0.19 0.25 0.03psych::describeBy(df_sbsa$profile_corr_facet_z, list(df_sbsa$rando, df_sbsa$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 342 0.27 0.67 0.21 0.25 0.64 -1.5 2.67 4.17 0.46 0.68 0.04
------------------------------------------------------------
: Self-Improvement
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 362 0.29 0.63 0.23 0.25 0.65 -1.06 2.44 3.5 0.56 0.27 0.03
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 326 0.49 0.76 0.45 0.46 0.72 -1.17 3.73 4.9 0.69 1.5 0.04
------------------------------------------------------------
: Self-Improvement
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 338 0.52 0.69 0.5 0.5 0.74 -1.22 2.54 3.77 0.17 -0.31 0.04lm_profile_corr_item <- lmerTest::lmer(profile_corr_item_z ~ time_d * rando + (1 | pid), data = df_sbsa)
lm_profile_corr_facet <- lmerTest::lmer(profile_corr_facet_z ~ time_d * rando + (1 | pid), data = df_sbsa)
kable(bind_rows(as.data.frame(summary(lm_profile_corr_item)$coefficients), as.data.frame(summary(lm_profile_corr_facet)$coefficients)) %>% as_tibble() %>% mutate(outcome = c(rep("Item-level profile corr.", 4), rep("Facet-level profile corr.", 4)), term = c(rep(c("Intercept", "time", "group", "time*group"), 2))) %>% select(outcome, term, everything()) %>% rename(p = `Pr(>|t|)`), digits = 3)| outcome | term | Estimate | Std. Error | df | t value | p |
|---|---|---|---|---|---|---|
| Item-level profile corr. | Intercept | 0.361 | 0.027 | 967.869 | 13.410 | 0.000 |
| Item-level profile corr. | time | 0.187 | 0.022 | 667.741 | 8.347 | 0.000 |
| Item-level profile corr. | group | 0.016 | 0.038 | 968.479 | 0.425 | 0.671 |
| Item-level profile corr. | time*group | -0.011 | 0.031 | 670.006 | -0.360 | 0.719 |
| Facet-level profile corr. | Intercept | 0.269 | 0.037 | 951.267 | 7.252 | 0.000 |
| Facet-level profile corr. | time | 0.229 | 0.030 | 667.445 | 7.648 | 0.000 |
| Facet-level profile corr. | group | 0.020 | 0.052 | 951.861 | 0.385 | 0.700 |
| Facet-level profile corr. | time*group | -0.003 | 0.042 | 669.591 | -0.080 | 0.937 |
Squared trait-/facet-differences by group and measurement occasion (mixed effects models) - results:
mod_traits_sqdiff_groups <- df_sbsa %>%
select(pid, time_d, rando, ends_with("_sqdiff")) %>%
pivot_longer(ends_with("_sqdiff"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
mod_traits_sqdiff_groups_unlist <- as.data.frame(summary(mod_traits_sqdiff_groups[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(mod_traits_sqdiff_groups)) {
mod_traits_sqdiff_groups_unlist <- bind_rows(mod_traits_sqdiff_groups_unlist,
as.data.frame(summary(mod_traits_sqdiff_groups[[i]])$coefficients) %>% as_tibble())
}
kable(mod_traits_sqdiff_groups_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
select(outcome, term, everything()) %>% rename(p = `Pr(>|t|)`) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | Estimate | Std. Error | df | t value | p |
|---|---|---|---|---|---|---|
| extraversion | Intercept | 1.878 | 0.103 | 959.798 | 18.152 | 0.000 |
| extraversion | time | -0.555 | 0.093 | 651.000 | -5.975 | 0.000 |
| extraversion | group | -0.064 | 0.145 | 959.798 | -0.438 | 0.661 |
| extraversion | time*group | 0.100 | 0.130 | 651.000 | 0.769 | 0.442 |
| agreeableness | Intercept | 0.589 | 0.047 | 972.211 | 12.538 | 0.000 |
| agreeableness | time | -0.150 | 0.043 | 651.000 | -3.497 | 0.001 |
| agreeableness | group | -0.063 | 0.066 | 972.211 | -0.963 | 0.336 |
| agreeableness | time*group | -0.021 | 0.060 | 651.000 | -0.351 | 0.726 |
| conscientiousness | Intercept | 1.873 | 0.108 | 919.365 | 17.281 | 0.000 |
| conscientiousness | time | -0.387 | 0.091 | 651.000 | -4.235 | 0.000 |
| conscientiousness | group | -0.348 | 0.152 | 919.365 | -2.287 | 0.022 |
| conscientiousness | time*group | 0.126 | 0.128 | 651.000 | 0.987 | 0.324 |
| neuroticism | Intercept | 3.407 | 0.183 | 854.426 | 18.585 | 0.000 |
| neuroticism | time | -0.699 | 0.136 | 651.000 | -5.131 | 0.000 |
| neuroticism | group | -0.099 | 0.257 | 854.426 | -0.384 | 0.701 |
| neuroticism | time*group | 0.053 | 0.191 | 651.000 | 0.276 | 0.782 |
| openness | Intercept | 0.490 | 0.039 | 1057.826 | 12.631 | 0.000 |
| openness | time | -0.129 | 0.040 | 651.000 | -3.258 | 0.001 |
| openness | group | -0.023 | 0.054 | 1057.826 | -0.423 | 0.673 |
| openness | time*group | 0.021 | 0.055 | 651.000 | 0.374 | 0.708 |
| sociability | Intercept | 2.992 | 0.166 | 929.743 | 18.009 | 0.000 |
| sociability | time | -0.911 | 0.142 | 651.000 | -6.398 | 0.000 |
| sociability | group | -0.137 | 0.233 | 929.743 | -0.589 | 0.556 |
| sociability | time*group | 0.190 | 0.200 | 651.000 | 0.953 | 0.341 |
| assertiveness | Intercept | 1.891 | 0.121 | 1060.149 | 15.604 | 0.000 |
| assertiveness | time | -0.559 | 0.124 | 651.000 | -4.517 | 0.000 |
| assertiveness | group | -0.151 | 0.170 | 1060.149 | -0.890 | 0.373 |
| assertiveness | time*group | 0.135 | 0.174 | 651.000 | 0.779 | 0.436 |
| energy | Intercept | 2.082 | 0.133 | 1045.145 | 15.642 | 0.000 |
| energy | time | -0.502 | 0.134 | 651.000 | -3.754 | 0.000 |
| energy | group | -0.015 | 0.187 | 1045.145 | -0.079 | 0.937 |
| energy | time*group | 0.022 | 0.187 | 651.000 | 0.119 | 0.906 |
| compassion | Intercept | 0.621 | 0.050 | 1119.816 | 12.439 | 0.000 |
| compassion | time | -0.193 | 0.055 | 651.000 | -3.543 | 0.000 |
| compassion | group | -0.143 | 0.070 | 1119.816 | -2.038 | 0.042 |
| compassion | time*group | 0.084 | 0.076 | 651.000 | 1.101 | 0.271 |
| respectfulness | Intercept | 0.666 | 0.060 | 1045.022 | 11.178 | 0.000 |
| respectfulness | time | -0.145 | 0.060 | 651.000 | -2.420 | 0.016 |
| respectfulness | group | 0.008 | 0.084 | 1045.022 | 0.099 | 0.921 |
| respectfulness | time*group | -0.039 | 0.084 | 651.000 | -0.468 | 0.640 |
| trust | Intercept | 1.250 | 0.096 | 1067.043 | 13.008 | 0.000 |
| trust | time | -0.276 | 0.099 | 651.000 | -2.784 | 0.006 |
| trust | group | 0.012 | 0.135 | 1067.043 | 0.092 | 0.927 |
| trust | time*group | -0.153 | 0.139 | 651.000 | -1.102 | 0.271 |
| organization | Intercept | 2.182 | 0.141 | 989.115 | 15.507 | 0.000 |
| organization | time | -0.478 | 0.132 | 651.000 | -3.633 | 0.000 |
| organization | group | -0.489 | 0.197 | 989.115 | -2.480 | 0.013 |
| organization | time*group | 0.204 | 0.185 | 651.000 | 1.107 | 0.269 |
| productiveness | Intercept | 3.108 | 0.172 | 901.841 | 18.114 | 0.000 |
| productiveness | time | -0.543 | 0.140 | 651.000 | -3.875 | 0.000 |
| productiveness | group | -0.383 | 0.241 | 901.841 | -1.593 | 0.112 |
| productiveness | time*group | 0.028 | 0.197 | 651.000 | 0.141 | 0.888 |
| responsibility | Intercept | 1.376 | 0.099 | 1011.321 | 13.859 | 0.000 |
| responsibility | time | -0.300 | 0.096 | 651.000 | -3.134 | 0.002 |
| responsibility | group | -0.210 | 0.139 | 1011.321 | -1.509 | 0.132 |
| responsibility | time*group | 0.107 | 0.134 | 651.000 | 0.798 | 0.425 |
| anxiety | Intercept | 4.092 | 0.227 | 947.361 | 17.991 | 0.000 |
| anxiety | time | -0.667 | 0.200 | 651.000 | -3.327 | 0.001 |
| anxiety | group | -0.066 | 0.319 | 947.361 | -0.208 | 0.835 |
| anxiety | time*group | -0.063 | 0.281 | 651.000 | -0.226 | 0.821 |
| depression | Intercept | 4.202 | 0.219 | 890.115 | 19.154 | 0.000 |
| depression | time | -0.986 | 0.175 | 651.000 | -5.618 | 0.000 |
| depression | group | -0.212 | 0.308 | 890.115 | -0.689 | 0.491 |
| depression | time*group | 0.166 | 0.246 | 651.000 | 0.674 | 0.501 |
| volatility | Intercept | 2.957 | 0.192 | 902.039 | 15.412 | 0.000 |
| volatility | time | -0.644 | 0.157 | 651.000 | -4.104 | 0.000 |
| volatility | group | -0.070 | 0.269 | 902.039 | -0.262 | 0.793 |
| volatility | time*group | 0.052 | 0.220 | 651.000 | 0.235 | 0.814 |
| curiosity | Intercept | 0.534 | 0.053 | 1232.150 | 10.166 | 0.000 |
| curiosity | time | -0.131 | 0.065 | 651.000 | -2.024 | 0.043 |
| curiosity | group | 0.025 | 0.074 | 1232.150 | 0.335 | 0.738 |
| curiosity | time*group | -0.049 | 0.091 | 651.000 | -0.535 | 0.593 |
| aesthetic | Intercept | 0.501 | 0.051 | 1248.444 | 9.892 | 0.000 |
| aesthetic | time | -0.167 | 0.064 | 651.000 | -2.623 | 0.009 |
| aesthetic | group | -0.066 | 0.071 | 1248.444 | -0.935 | 0.350 |
| aesthetic | time*group | 0.076 | 0.089 | 651.000 | 0.851 | 0.395 |
| imagination | Intercept | 1.310 | 0.104 | 969.568 | 12.540 | 0.000 |
| imagination | time | -0.319 | 0.095 | 651.000 | -3.350 | 0.001 |
| imagination | group | -0.103 | 0.147 | 969.568 | -0.702 | 0.483 |
| imagination | time*group | 0.158 | 0.133 | 651.000 | 1.184 | 0.237 |
Adapt latent change score model from above (but without any moderation) and add grouping factor in estimation (adding vectorized equality constraints to the model step by step):
# create templates:
# configural invariance
trait_template_main_config <- '
trait_t1 =~ 1*ind01_t1 + c("lamb2a", "lamb2b")*ind02_t1 + c("lamb3a", "lamb3b")*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + c("lamb2a", "lamb2b")*ind02_t2 + c("lamb3a", "lamb3b")*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
trait_t1 ~ c("b5_int_a", "b5_int_b")*1 # This estimates the intercept of trait_t1
d_trait_1 ~~ c("d_var_a", "d_var_b")*d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ c("b5_var_a", "b5_var_b")*trait_t1 # This estimates the variance of trait_t1
d_trait_1 ~~ c("fb_a", "fb_b")* trait_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
ind01_t1 ~~ c("cov1a", "cov1b")*ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ c("cov2a", "cov2b")*ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ c("cov3a", "cov3b")*ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ c("res1a", "res1b")*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ c("res2a", "res2b")*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ c("res3a", "res3b")*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ c("res1a", "res1b")*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ c("res2a", "res2b")*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ c("res3a", "res3b")*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
# weak invariance
trait_template_main_weak <- '
trait_t1 =~ 1*ind01_t1 + c("lamb2", "lamb2")*ind02_t1 + c("lamb3", "lamb3")*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + c("lamb2", "lamb2")*ind02_t2 + c("lamb3", "lamb3")*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
trait_t1 ~ c("b5_int_a", "b5_int_b")*1 # This estimates the intercept of trait_t1
d_trait_1 ~~ c("d_var_a", "d_var_b")*d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ c("b5_var_a", "b5_var_b")*trait_t1 # This estimates the variance of trait_t1
d_trait_1 ~~ c("fb_a", "fb_b")* trait_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
ind01_t1 ~~ c("cov1a", "cov1b")*ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ c("cov2a", "cov2b")*ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ c("cov3a", "cov3b")*ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ c("res1a", "res1b")*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ c("res2a", "res2b")*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ c("res3a", "res3b")*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ c("res1a", "res1b")*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ c("res2a", "res2b")*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ c("res3a", "res3b")*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
# strong invariance
trait_template_main_strong <- '
trait_t1 =~ 1*ind01_t1 + c("lamb2", "lamb2")*ind02_t1 + c("lamb3", "lamb3")*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + c("lamb2", "lamb2")*ind02_t2 + c("lamb3", "lamb3")*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
trait_t1 ~ c("b5_int", "b5_int")*1 # This estimates the intercept of trait_t1
d_trait_1 ~~ c("d_var_a", "d_var_b")*d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ c("b5_var_a", "b5_var_b")*trait_t1 # This estimates the variance of trait_t1
d_trait_1 ~~ c("fb_a", "fb_b")* trait_t1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
ind01_t1 ~~ c("cov1a", "cov1b")*ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ c("cov2a", "cov2b")*ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ c("cov3a", "cov3b")*ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ c("res1a", "res1b")*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ c("res2a", "res2b")*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ c("res3a", "res3b")*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ c("res1a", "res1b")*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ c("res2a", "res2b")*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ c("res3a", "res3b")*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
# strict invariance -> not really needed for this analysis
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# configural - current
template_filled_config_current <- str_replace_all(trait_template_main_config,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_main_fit_config_current <- lavaan(template_filled_config_current,
data = bind_rows(df_sbsa_wide_pers_sa_mod, df_sbsa_wide_pers_sb_mod) %>% left_join(group_assign),
estimator='mlr', fixed.x=FALSE, missing='fiml', group = "rando")
eval(call("<-", as.name(paste0("lcs_", short_name, "_main_config_current")), template_filled_config_current))
eval(call("<-", as.name(paste0("fit_lcs_", short_name, "_main_config_current")), trait_main_fit_config_current))
# configural - ideal
template_filled_config_ideal <- str_replace_all(trait_template_main_config,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_main_fit_config_ideal <- lavaan(template_filled_config_ideal,
data = bind_rows(df_sbsa_wide_pers_sa_mod, df_sbsa_wide_pers_sb_mod) %>% left_join(group_assign),
estimator='mlr', fixed.x=FALSE, missing='fiml', group = "rando")
eval(call("<-", as.name(paste0("lcs_", short_name, "_main_config_ideal")), template_filled_config_ideal))
eval(call("<-", as.name(paste0("fit_lcs_", short_name, "_main_config_ideal")), trait_main_fit_config_ideal))
# weak - current
template_filled_weak_current <- str_replace_all(trait_template_main_weak,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_main_fit_weak_current <- lavaan(template_filled_weak_current,
data = bind_rows(df_sbsa_wide_pers_sa_mod, df_sbsa_wide_pers_sb_mod) %>% left_join(group_assign),
estimator='mlr', fixed.x=FALSE, missing='fiml', group = "rando", group.equal = "loadings")
eval(call("<-", as.name(paste0("lcs_", short_name, "_main_weak_current")), template_filled_weak_current))
eval(call("<-", as.name(paste0("fit_lcs_", short_name, "_main_weak_current")), trait_main_fit_weak_current))
# weak - ideal
template_filled_weak_ideal <- str_replace_all(trait_template_main_weak,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_main_fit_weak_ideal <- lavaan(template_filled_weak_ideal,
data = bind_rows(df_sbsa_wide_pers_sa_mod, df_sbsa_wide_pers_sb_mod) %>% left_join(group_assign),
estimator='mlr', fixed.x=FALSE, missing='fiml', group = "rando", group.equal = "loadings")
eval(call("<-", as.name(paste0("lcs_", short_name, "_main_weak_ideal")), template_filled_weak_ideal))
eval(call("<-", as.name(paste0("fit_lcs_", short_name, "_main_weak_ideal")), trait_main_fit_weak_ideal))
# strong - current
template_filled_strong_current <- str_replace_all(trait_template_main_strong,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_main_fit_strong_current <- lavaan(template_filled_strong_current,
data = bind_rows(df_sbsa_wide_pers_sa_mod, df_sbsa_wide_pers_sb_mod) %>% left_join(group_assign),
estimator='mlr', fixed.x=FALSE, missing='fiml', group = "rando", group.equal = c("intercepts", "loadings"))
eval(call("<-", as.name(paste0("lcs_", short_name, "_main_strong_current")), template_filled_strong_current))
eval(call("<-", as.name(paste0("fit_lcs_", short_name, "_main_strong_current")), trait_main_fit_strong_current))
# strong - ideal
template_filled_strong_ideal <- str_replace_all(trait_template_main_strong,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_main_fit_strong_ideal <- lavaan(template_filled_strong_ideal,
data = bind_rows(df_sbsa_wide_pers_sa_mod, df_sbsa_wide_pers_sb_mod) %>% left_join(group_assign),
estimator='mlr', fixed.x=FALSE, missing='fiml', group = "rando", group.equal = c("intercepts", "loadings"))
eval(call("<-", as.name(paste0("lcs_", short_name, "_main_strong_ideal")), template_filled_strong_ideal))
eval(call("<-", as.name(paste0("fit_lcs_", short_name, "_main_strong_ideal")), trait_main_fit_strong_ideal))
}Extraversion (current self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_extra_main_config_current, fit_lcs_extra_main_weak_current, fit_lcs_extra_main_strong_current)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_extra_main_config_current 24 7453.0 7590.2 33.135
fit_lcs_extra_main_weak_current 26 7453.3 7581.3 37.440 3.9027 2
fit_lcs_extra_main_strong_current 30 7445.7 7555.5 37.847 0.3958 4
Pr(>Chisq)
fit_lcs_extra_main_config_current
fit_lcs_extra_main_weak_current 0.1421
fit_lcs_extra_main_strong_current 0.9828# show model with varying latent change parameters
# -> key parameter is "d_extra_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_extra_current <- broom::tidy(fit_lcs_extra_main_weak_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_extra_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_extra_current, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_extra_1 ~1 | d_int_a | 0.682 | 1.941 | 5.712 | 0 | |
| d_extra_1 ~1 | d_int_b | 0.655 | 1.801 | 5.031 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_extra_main_strong_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_extra_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_extra_1 ~1 | d_int | 0.67 | 1.911 | 7.584 | 0 |
| d_extra_1 ~1 | d_int | 0.67 | 1.834 | 7.584 | 0 |
Agreeableness (current self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_agree_main_config_current, fit_lcs_agree_main_weak_current, fit_lcs_agree_main_strong_current)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_agree_main_config_current 24 6350.6 6487.7 46.417
fit_lcs_agree_main_weak_current 26 6346.9 6474.9 46.754 0.26941 2
fit_lcs_agree_main_strong_current 30 6341.8 6451.6 49.710 2.43589 4
Pr(>Chisq)
fit_lcs_agree_main_config_current
fit_lcs_agree_main_weak_current 0.8740
fit_lcs_agree_main_strong_current 0.6562# show model with varying latent change parameters
# -> key parameter is "d_agree_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_agree_current <- broom::tidy(fit_lcs_agree_main_weak_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_agree_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_agree_current, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_agree_1 ~1 | d_int_a | 0.553 | 2.566 | 2.007 | 0.045 | |
| d_agree_1 ~1 | d_int_b | 0.705 | 3.333 | 4.770 | 0.000 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_agree_main_strong_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_agree_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_agree_1 ~1 | d_int | 0.639 | 2.883 | 4.245 | 0 |
| d_agree_1 ~1 | d_int | 0.639 | 3.096 | 4.245 | 0 |
Conscientiousness (current self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_consc_main_config_current, fit_lcs_consc_main_weak_current, fit_lcs_consc_main_strong_current)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_consc_main_config_current 24 6865.2 7002.4 43.058
fit_lcs_consc_main_weak_current 26 6865.4 6993.4 47.284 4.3031 2
fit_lcs_consc_main_strong_current 30 6858.7 6968.4 48.571 1.2081 4
Pr(>Chisq)
fit_lcs_consc_main_config_current
fit_lcs_consc_main_weak_current 0.1163
fit_lcs_consc_main_strong_current 0.8768# show model with varying latent change parameters
# -> key parameter is "d_consc_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_consc_current <- broom::tidy(fit_lcs_consc_main_weak_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_consc_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_consc_current, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_consc_1 ~1 | d_int_a | 0.575 | 1.594 | 4.068 | 0 | |
| d_consc_1 ~1 | d_int_b | 0.663 | 1.888 | 4.967 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_consc_main_strong_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_consc_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_consc_1 ~1 | d_int | 0.617 | 1.692 | 6.28 | 0 |
| d_consc_1 ~1 | d_int | 0.617 | 1.778 | 6.28 | 0 |
neuroticism (current self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_neuro_main_config_current, fit_lcs_neuro_main_weak_current, fit_lcs_neuro_main_strong_current)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_neuro_main_config_current 24 7741.0 7878.2 30.316
fit_lcs_neuro_main_weak_current 26 7738.3 7866.3 31.583 1.3185 2
fit_lcs_neuro_main_strong_current 30 7732.6 7842.3 33.910 2.3157 4
Pr(>Chisq)
fit_lcs_neuro_main_config_current
fit_lcs_neuro_main_weak_current 0.5172
fit_lcs_neuro_main_strong_current 0.6779# show model with varying latent change parameters
# -> key parameter is "d_neuro_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_neuro_current <- broom::tidy(fit_lcs_neuro_main_weak_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_neuro_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_neuro_current, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_neuro_1 ~1 | d_int_a | 0.391 | 0.863 | 2.628 | 0.009 | |
| d_neuro_1 ~1 | d_int_b | 0.548 | 1.135 | 4.128 | 0.000 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_neuro_main_strong_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_neuro_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_neuro_1 ~1 | d_int | 0.473 | 1.027 | 4.741 | 0 |
| d_neuro_1 ~1 | d_int | 0.473 | 0.992 | 4.741 | 0 |
Openness (current self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_openn_main_config_current, fit_lcs_openn_main_weak_current, fit_lcs_openn_main_strong_current)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_openn_main_config_current 24 6939.0 7076.2 33.624
fit_lcs_openn_main_weak_current 26 6938.7 7066.8 37.379 3.1544 2
fit_lcs_openn_main_strong_current 30 6943.5 7053.2 50.107 12.8972 4
Pr(>Chisq)
fit_lcs_openn_main_config_current
fit_lcs_openn_main_weak_current 0.20655
fit_lcs_openn_main_strong_current 0.01179 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# show model with varying latent change parameters
# -> key parameter is "d_openn_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_openn_current <- broom::tidy(fit_lcs_openn_main_weak_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_openn_1 ~1 ")) %>% mutate(sig_diff = "*")
kable(params_main_lcs_openn_current, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_openn_1 ~1 | d_int_a | 0.812 | 2.850 | 4.354 | 0.000 | * |
| d_openn_1 ~1 | d_int_b | 0.060 | 0.287 | 0.418 | 0.676 | * |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_openn_main_strong_current, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_openn_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_openn_1 ~1 | d_int | 0.44 | 1.67 | 3.719 | 0 |
| d_openn_1 ~1 | d_int | 0.44 | 1.87 | 3.719 | 0 |
Extraversion (ideal self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_extra_main_config_ideal, fit_lcs_extra_main_weak_ideal, fit_lcs_extra_main_strong_ideal)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_extra_main_config_ideal 24 6564.7 6701.9 37.286
fit_lcs_extra_main_weak_ideal 26 6561.0 6689.0 37.579 0.2714 2
fit_lcs_extra_main_strong_ideal 30 6567.4 6677.1 51.956 13.5083 4
Pr(>Chisq)
fit_lcs_extra_main_config_ideal
fit_lcs_extra_main_weak_ideal 0.873105
fit_lcs_extra_main_strong_ideal 0.009042 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# show model with varying latent change parameters
# -> key parameter is "d_extra_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_extra_ideal <- broom::tidy(fit_lcs_extra_main_weak_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_extra_1 ~1 ")) %>% mutate(sig_diff = "**")
kable(params_main_lcs_extra_ideal, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_extra_1 ~1 | d_int_a | 1.507 | 3.943 | 5.331 | 0.000 | ** |
| d_extra_1 ~1 | d_int_b | 0.414 | 1.385 | 1.459 | 0.145 | ** |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_extra_main_strong_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_extra_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_extra_1 ~1 | d_int | 0.909 | 2.690 | 4.199 | 0 |
| d_extra_1 ~1 | d_int | 0.909 | 2.692 | 4.199 | 0 |
Agreeableness (ideal self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_agree_main_config_ideal, fit_lcs_agree_main_weak_ideal, fit_lcs_agree_main_strong_ideal)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_agree_main_config_ideal 24 6463.0 6600.1 23.251
fit_lcs_agree_main_weak_ideal 26 6460.5 6588.5 24.761 1.2897 2
fit_lcs_agree_main_strong_ideal 30 6454.4 6564.1 26.639 1.6888 4
Pr(>Chisq)
fit_lcs_agree_main_config_ideal
fit_lcs_agree_main_weak_ideal 0.5247
fit_lcs_agree_main_strong_ideal 0.7927# show model with varying latent change parameters
# -> key parameter is "d_agree_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_agree_ideal <- broom::tidy(fit_lcs_agree_main_weak_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_agree_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_agree_ideal, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_agree_1 ~1 | d_int_a | 0.811 | 2.664 | 4.032 | 0 | |
| d_agree_1 ~1 | d_int_b | 0.989 | 3.388 | 3.855 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_agree_main_strong_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_agree_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_agree_1 ~1 | d_int | 0.907 | 2.921 | 5.453 | 0 |
| d_agree_1 ~1 | d_int | 0.907 | 3.161 | 5.453 | 0 |
Conscientiousness (ideal self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_consc_main_config_ideal, fit_lcs_consc_main_weak_ideal, fit_lcs_consc_main_strong_ideal)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_consc_main_config_ideal 24 5537.1 5674.2 34.915
fit_lcs_consc_main_weak_ideal 26 5535.2 5663.2 37.032 1.5333 2
fit_lcs_consc_main_strong_ideal 30 5529.9 5639.7 39.771 1.8257 4
Pr(>Chisq)
fit_lcs_consc_main_config_ideal
fit_lcs_consc_main_weak_ideal 0.4646
fit_lcs_consc_main_strong_ideal 0.7678# show model with varying latent change parameters
# -> key parameter is "d_consc_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_consc_ideal <- broom::tidy(fit_lcs_consc_main_weak_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_consc_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_consc_ideal, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_consc_1 ~1 | d_int_a | 1.302 | 3.836 | 3.002 | 0.003 | |
| d_consc_1 ~1 | d_int_b | 1.505 | 4.367 | 4.888 | 0.000 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_consc_main_strong_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_consc_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_consc_1 ~1 | d_int | 1.404 | 4.058 | 5.286 | 0 |
| d_consc_1 ~1 | d_int | 1.404 | 4.159 | 5.286 | 0 |
neuroticism (ideal self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_neuro_main_config_ideal, fit_lcs_neuro_main_weak_ideal, fit_lcs_neuro_main_strong_ideal)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_neuro_main_config_ideal 24 5779.9 5917.0 46.031
fit_lcs_neuro_main_weak_ideal 26 5777.0 5905.0 47.162 1.0171 2
fit_lcs_neuro_main_strong_ideal 30 5771.8 5881.5 49.926 2.5394 4
Pr(>Chisq)
fit_lcs_neuro_main_config_ideal
fit_lcs_neuro_main_weak_ideal 0.6014
fit_lcs_neuro_main_strong_ideal 0.6376# show model with varying latent change parameters
# -> key parameter is "d_neuro_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_neuro_ideal <- broom::tidy(fit_lcs_neuro_main_weak_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_neuro_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_neuro_ideal, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_neuro_1 ~1 | d_int_a | 0.459 | 1.467 | 3.657 | 0 | |
| d_neuro_1 ~1 | d_int_b | 0.476 | 1.422 | 4.256 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_neuro_main_strong_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_neuro_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_neuro_1 ~1 | d_int | 0.47 | 1.494 | 5.583 | 0 |
| d_neuro_1 ~1 | d_int | 0.47 | 1.408 | 5.583 | 0 |
Openness (ideal self): results
# model comparison tests for measurement invariance
lavTestLRT(fit_lcs_openn_main_config_ideal, fit_lcs_openn_main_weak_ideal, fit_lcs_openn_main_strong_ideal)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff
fit_lcs_openn_main_config_ideal 24 6662.5 6799.7 36.707
fit_lcs_openn_main_weak_ideal 26 6659.5 6787.5 37.690 0.9424 2
fit_lcs_openn_main_strong_ideal 30 6656.2 6765.9 42.399 4.2416 4
Pr(>Chisq)
fit_lcs_openn_main_config_ideal
fit_lcs_openn_main_weak_ideal 0.6243
fit_lcs_openn_main_strong_ideal 0.3743# show model with varying latent change parameters
# -> key parameter is "d_openn_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
params_main_lcs_openn_ideal <- broom::tidy(fit_lcs_openn_main_weak_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_openn_1 ~1 ")) %>% mutate(sig_diff = " ")
kable(params_main_lcs_openn_ideal, digits = 3) | term | label | estimate | std.all | statistic | p.value | sig_diff |
|---|---|---|---|---|---|---|
| d_openn_1 ~1 | d_int_a | 0.683 | 2.817 | 2.743 | 0.006 | |
| d_openn_1 ~1 | d_int_b | 0.841 | 3.433 | 2.913 | 0.004 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(fit_lcs_openn_main_strong_ideal, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_openn_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_openn_1 ~1 | d_int | 0.776 | 3.134 | 4.024 | 0 |
| d_openn_1 ~1 | d_int | 0.776 | 3.219 | 4.024 | 0 |
params_main_summary <- bind_rows(
params_main_lcs_extra_current %>% mutate(trait = names(b5_vars)[1], self = "current") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_extra_ideal %>% mutate(trait = names(b5_vars)[1], self = "ideal") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_agree_current %>% mutate(trait = names(b5_vars)[2], self = "current") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_agree_ideal %>% mutate(trait = names(b5_vars)[2], self = "ideal") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_consc_current %>% mutate(trait = names(b5_vars)[3], self = "current") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_consc_ideal %>% mutate(trait = names(b5_vars)[3], self = "ideal") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_neuro_current %>% mutate(trait = names(b5_vars)[4], self = "current") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_neuro_ideal %>% mutate(trait = names(b5_vars)[4], self = "ideal") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_openn_current %>% mutate(trait = names(b5_vars)[5], self = "current") %>% select(trait, self, group = label, estimate, p.value, sig_diff),
params_main_lcs_openn_ideal %>% mutate(trait = names(b5_vars)[5], self = "ideal") %>% select(trait, self, group = label, estimate, p.value, sig_diff)
) %>% mutate(group = ifelse(group=="d_int_a", "accept", "improve"))
kable(params_main_summary, digits = 3)| trait | self | group | estimate | p.value | sig_diff |
|---|---|---|---|---|---|
| extraversion | current | accept | 0.682 | 0.000 | |
| extraversion | current | improve | 0.655 | 0.000 | |
| extraversion | ideal | accept | 1.507 | 0.000 | ** |
| extraversion | ideal | improve | 0.414 | 0.145 | ** |
| agreeableness | current | accept | 0.553 | 0.045 | |
| agreeableness | current | improve | 0.705 | 0.000 | |
| agreeableness | ideal | accept | 0.811 | 0.000 | |
| agreeableness | ideal | improve | 0.989 | 0.000 | |
| conscientiousness | current | accept | 0.575 | 0.000 | |
| conscientiousness | current | improve | 0.663 | 0.000 | |
| conscientiousness | ideal | accept | 1.302 | 0.003 | |
| conscientiousness | ideal | improve | 1.505 | 0.000 | |
| neuroticism | current | accept | 0.391 | 0.009 | |
| neuroticism | current | improve | 0.548 | 0.000 | |
| neuroticism | ideal | accept | 0.459 | 0.000 | |
| neuroticism | ideal | improve | 0.476 | 0.000 | |
| openness | current | accept | 0.812 | 0.000 | * |
| openness | current | improve | 0.060 | 0.676 | * |
| openness | ideal | accept | 0.683 | 0.006 | |
| openness | ideal | improve | 0.841 | 0.004 |
We will explore b) whether the extent of change/acceptance is related to personal project dimension variables.
For now, I use the personal project dimension variables assessed at T1:
They were, however, also assessed at T2:
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sb_ppd <- df_sbsa %>%
filter(rando=="Self-Improvement") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb01"))) %>% # Personal project dimensions - self improvement
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sb01")))) %>%
select(-c(sb01_01_t2, sb01_02_t2))
# colnames(df_sbsa_wide_pers_sb_ppd)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Improvement") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sb_ppd)
# need to form a mean score because some models did not converge when using the latent factor of PPD (but moderate to high correlation between the two items)
df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers_sb_ppd %>% mutate(ppd = rowMeans(across(c(sb01_01_t1, sb01_02_t1)), na.rm=T))Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific change goals
trait_template_ppd_improve <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~~ d_trait_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
trait_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_trait_1 ~ ppd # This estimates the moderation effect on the change score
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_ppd_improve,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), trait_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 19 | 4683.753 | 4758.007 | 0.994 | 0.034 | 0.031 |
# parameters of interest
params_lcs_extra_curr_ppd <- broom::tidy(fit_mi_lcs_extra_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~ ppd", "ppd ~~ ppd", "extra_t1 ~ ppd", # change ppd
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.610 | NA | NA |
| d_extra_1 ~1 | -0.178 | -0.367 | 0.011 | -0.507 | -1.843 | 0.065 |
| extra_t1 ~1 | 3.306 | 3.050 | 3.561 | 5.722 | 25.397 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.116 | 0.072 | 0.161 | 0.943 | 5.151 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.050 | -0.080 | -0.020 | -0.264 | -3.282 | 0.001 |
| extra_t1 ~ ppd | -0.159 | -0.230 | -0.088 | -0.255 | -4.399 | 0.000 |
| d_extra_1 ~ ppd | 0.090 | 0.032 | 0.148 | 0.238 | 3.040 | 0.002 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.448 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 19 | 4167.488 | 4241.742 | 0.982 | 0.052 | 0.039 |
# parameters of interest
params_lcs_agree_curr_ppd <- broom::tidy(fit_mi_lcs_agree_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~ ppd", "ppd ~~ ppd", "agree_t1 ~ ppd", # change ppd
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.061 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_agree_1 ~1 | 0.083 | -0.051 | 0.217 | 0.387 | 1.218 | 0.223 |
| agree_t1 ~1 | 3.481 | 3.262 | 3.701 | 8.242 | 31.072 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.046 | 0.023 | 0.070 | 1.000 | 3.834 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.033 | -0.051 | -0.016 | -0.367 | -3.696 | 0.000 |
| agree_t1 ~ ppd | -0.036 | -0.096 | 0.024 | -0.079 | -1.182 | 0.237 |
| d_agree_1 ~ ppd | -0.002 | -0.040 | 0.036 | -0.008 | -0.098 | 0.922 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.436 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 19 | 4379.512 | 4453.765 | 0.998 | 0.025 | 0.024 |
# parameters of interest
params_lcs_consc_curr_ppd <- broom::tidy(fit_mi_lcs_consc_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~ ppd", "ppd ~~ ppd", "consc_t1 ~ ppd", # change ppd
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.507 | NA | NA |
| d_consc_1 ~1 | 0.047 | -0.117 | 0.210 | 0.129 | 0.559 | 0.576 |
| consc_t1 ~1 | 4.257 | 3.968 | 4.547 | 5.754 | 28.822 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.130 | 0.086 | 0.174 | 0.997 | 5.770 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.081 | -0.122 | -0.039 | -0.313 | -3.826 | 0.000 |
| consc_t1 ~ ppd | -0.204 | -0.287 | -0.121 | -0.256 | -4.799 | 0.000 |
| d_consc_1 ~ ppd | 0.022 | -0.026 | 0.071 | 0.057 | 0.902 | 0.367 |
| ppd ~~ ppd | 0.858 | 0.742 | 0.975 | 1.000 | 14.423 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 19 | 4836.495 | 4910.749 | 0.996 | 0.036 | 0.023 |
# parameters of interest
params_lcs_neuro_curr_ppd <- broom::tidy(fit_mi_lcs_neuro_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~ ppd", "ppd ~~ ppd", "neuro_t1 ~ ppd", # change ppd
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.029 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_neuro_1 ~1 | 0.085 | -0.128 | 0.298 | 0.172 | 0.779 | 0.436 |
| neuro_t1 ~1 | 2.507 | 2.161 | 2.853 | 3.120 | 14.202 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.235 | 0.164 | 0.306 | 0.974 | 6.506 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.117 | -0.169 | -0.066 | -0.319 | -4.484 | 0.000 |
| neuro_t1 ~ ppd | 0.285 | 0.190 | 0.380 | 0.329 | 5.868 | 0.000 |
| d_neuro_1 ~ ppd | -0.086 | -0.148 | -0.023 | -0.162 | -2.680 | 0.007 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.450 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 19 | 4487.495 | 4561.749 | 0.99 | 0.044 | 0.024 |
# parameters of interest
params_lcs_openn_curr_ppd <- broom::tidy(fit_mi_lcs_openn_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~ ppd", "ppd ~~ ppd", "openn_t1 ~ ppd", # change ppd
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.913 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.410 | NA | NA |
| d_openn_1 ~1 | 0.067 | -0.060 | 0.194 | 0.342 | 1.036 | 0.300 |
| openn_t1 ~1 | 3.468 | 3.269 | 3.667 | 7.975 | 34.200 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.038 | 0.018 | 0.059 | 1.000 | 3.621 | 0.000 |
| openn_t1 ~~ d_openn_1 | 0.000 | -0.016 | 0.015 | -0.003 | -0.027 | 0.978 |
| openn_t1 ~ ppd | -0.010 | -0.064 | 0.045 | -0.020 | -0.343 | 0.731 |
| d_openn_1 ~ ppd | -0.003 | -0.040 | 0.034 | -0.014 | -0.151 | 0.880 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.443 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
facet_template_ppd_improve <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
facet_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_facet_1 ~ ppd # This estimates the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
template_filled <- str_replace_all(facet_template_ppd_improve,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), facet_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8635.355 | 8725.241 | 0.945 | 0.078 | 0.047 |
# parameters of interest
params_lcs_socia_curr_ppd <- broom::tidy(fit_mi_lcs_socia_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~ ppd", "ppd ~~ ppd", "d_socia_1 ~ ppd", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.028 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.647 | NA | NA |
| d_socia_1 ~1 | -0.166 | -0.489 | 0.157 | -0.314 | -1.009 | 0.313 |
| socia_t1 ~1 | 3.128 | 2.716 | 3.540 | 3.718 | 14.874 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.271 | 0.162 | 0.381 | 0.969 | 4.873 | 0.000 |
| socia_t1 ~~ d_socia_1 | -0.151 | -0.232 | -0.070 | -0.347 | -3.647 | 0.000 |
| d_socia_1 ~ ppd | 0.101 | 0.003 | 0.198 | 0.176 | 2.025 | 0.043 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.445 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8940.366 | 9030.252 | 0.998 | 0.011 | 0.036 |
# parameters of interest
params_lcs_asser_curr_ppd <- broom::tidy(fit_mi_lcs_asser_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~ ppd", "ppd ~~ ppd", "d_asser_1 ~ ppd", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.424 | NA | NA |
| d_asser_1 ~1 | -0.193 | -0.392 | 0.005 | -0.794 | -1.908 | 0.056 |
| asser_t1 ~1 | 3.520 | 3.240 | 3.801 | 6.159 | 24.604 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.055 | 0.007 | 0.103 | 0.925 | 2.230 | 0.026 |
| asser_t1 ~~ d_asser_1 | -0.019 | -0.055 | 0.016 | -0.148 | -1.074 | 0.283 |
| d_asser_1 ~ ppd | 0.072 | 0.011 | 0.133 | 0.274 | 2.328 | 0.020 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.440 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8907.562 | 8997.448 | 0.971 | 0.048 | 0.038 |
# parameters of interest
params_lcs_energ_curr_ppd <- broom::tidy(fit_mi_lcs_energ_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~ ppd", "ppd ~~ ppd", "d_energ_1 ~ ppd", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.553 | NA | NA |
| d_energ_1 ~1 | 0.041 | -0.099 | 0.181 | 0.185 | 0.577 | 0.564 |
| energ_t1 ~1 | 2.189 | 1.945 | 2.433 | 5.462 | 17.554 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.048 | 0.011 | 0.086 | 0.974 | 2.524 | 0.012 |
| energ_t1 ~~ d_energ_1 | -0.019 | -0.039 | 0.001 | -0.231 | -1.840 | 0.066 |
| d_energ_1 ~ ppd | -0.039 | -0.082 | 0.004 | -0.162 | -1.772 | 0.076 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.445 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8653.208 | 8743.094 | 0.98 | 0.034 | 0.042 |
# parameters of interest
params_lcs_compa_curr_ppd <- broom::tidy(fit_mi_lcs_compa_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~ ppd", "ppd ~~ ppd", "d_compa_1 ~ ppd", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.139 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.403 | NA | NA |
| d_compa_1 ~1 | -0.053 | -0.302 | 0.197 | -0.208 | -0.413 | 0.679 |
| compa_t1 ~1 | 4.232 | 3.853 | 4.611 | 5.913 | 21.908 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.062 | -0.015 | 0.140 | 0.972 | 1.576 | 0.115 |
| compa_t1 ~~ d_compa_1 | -0.090 | -0.166 | -0.013 | -0.503 | -2.303 | 0.021 |
| d_compa_1 ~ ppd | 0.045 | -0.026 | 0.116 | 0.166 | 1.253 | 0.210 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.443 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 7909.711 | 7999.597 | 0.935 | 0.072 | 0.058 |
# parameters of interest
params_lcs_respe_curr_ppd <- broom::tidy(fit_mi_lcs_respe_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~ ppd", "ppd ~~ ppd", "d_respe_1 ~ ppd", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.127 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_respe_1 ~1 | 0.133 | -0.056 | 0.322 | 0.407 | 1.378 | 0.168 |
| respe_t1 ~1 | 4.569 | 4.290 | 4.848 | 7.834 | 32.044 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.106 | 0.052 | 0.161 | 0.997 | 3.830 | 0.000 |
| respe_t1 ~~ d_respe_1 | -0.090 | -0.149 | -0.032 | -0.477 | -3.029 | 0.002 |
| d_respe_1 ~ ppd | -0.020 | -0.075 | 0.036 | -0.056 | -0.694 | 0.488 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.443 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 9020.237 | 9110.123 | 0.956 | 0.055 | 0.043 |
# parameters of interest
params_lcs_trust_curr_ppd <- broom::tidy(fit_mi_lcs_trust_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~ ppd", "ppd ~~ ppd", "d_trust_1 ~ ppd", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.559 | NA | NA |
| d_trust_1 ~1 | -0.133 | -0.349 | 0.084 | -0.408 | -1.203 | 0.229 |
| trust_t1 ~1 | 2.807 | 2.496 | 3.119 | 4.703 | 17.669 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.106 | 0.014 | 0.197 | 1.000 | 2.261 | 0.024 |
| trust_t1 ~~ d_trust_1 | -0.062 | -0.123 | -0.001 | -0.318 | -1.980 | 0.048 |
| d_trust_1 ~ ppd | 0.006 | -0.059 | 0.071 | 0.018 | 0.186 | 0.853 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.444 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8564.569 | 8654.455 | 0.977 | 0.055 | 0.034 |
# parameters of interest
params_lcs_organ_curr_ppd <- broom::tidy(fit_mi_lcs_organ_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~ ppd", "ppd ~~ ppd", "d_organ_1 ~ ppd", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.077 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.542 | NA | NA |
| d_organ_1 ~1 | 0.133 | -0.140 | 0.407 | 0.257 | 0.954 | 0.340 |
| organ_t1 ~1 | 2.139 | 1.693 | 2.585 | 2.070 | 9.400 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.265 | 0.148 | 0.381 | 0.980 | 4.450 | 0.000 |
| organ_t1 ~~ d_organ_1 | -0.194 | -0.289 | -0.100 | -0.373 | -4.026 | 0.000 |
| d_organ_1 ~ ppd | -0.079 | -0.159 | 0.001 | -0.141 | -1.931 | 0.053 |
| ppd ~~ ppd | 0.858 | 0.742 | 0.975 | 1.000 | 14.437 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8485.898 | 8575.784 | 0.976 | 0.05 | 0.037 |
# parameters of interest
params_lcs_produ_curr_ppd <- broom::tidy(fit_mi_lcs_produ_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~ ppd", "ppd ~~ ppd", "d_produ_1 ~ ppd", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.050 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.512 | NA | NA |
| d_produ_1 ~1 | -0.010 | -0.249 | 0.230 | -0.025 | -0.078 | 0.938 |
| produ_t1 ~1 | 2.511 | 2.161 | 2.862 | 3.268 | 14.040 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.139 | 0.070 | 0.208 | 0.990 | 3.953 | 0.000 |
| produ_t1 ~~ d_produ_1 | -0.089 | -0.149 | -0.028 | -0.324 | -2.870 | 0.004 |
| d_produ_1 ~ ppd | -0.041 | -0.111 | 0.028 | -0.102 | -1.163 | 0.245 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.444 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8627.266 | 8717.152 | 0.913 | 0.083 | 0.081 |
# parameters of interest
params_lcs_respo_curr_ppd <- broom::tidy(fit_mi_lcs_respo_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~ ppd", "ppd ~~ ppd", "d_respo_1 ~ ppd", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.465 | NA | NA |
| d_respo_1 ~1 | 0.172 | 0.014 | 0.329 | 0.821 | 2.133 | 0.033 |
| respo_t1 ~1 | 3.964 | 3.724 | 4.203 | 8.937 | 32.420 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.043 | 0.004 | 0.081 | 0.973 | 2.150 | 0.032 |
| respo_t1 ~~ d_respo_1 | -0.022 | -0.048 | 0.003 | -0.248 | -1.707 | 0.088 |
| d_respo_1 ~ ppd | -0.037 | -0.083 | 0.008 | -0.165 | -1.609 | 0.108 |
| ppd ~~ ppd | 0.858 | 0.742 | 0.975 | 1.000 | 14.415 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8952.143 | 9042.029 | 0.983 | 0.038 | 0.039 |
# parameters of interest
params_lcs_anxie_curr_ppd <- broom::tidy(fit_mi_lcs_anxie_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~ ppd", "ppd ~~ ppd", "d_anxie_1 ~ ppd", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.075 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.680 | NA | NA |
| d_anxie_1 ~1 | -0.170 | -0.485 | 0.145 | -0.292 | -1.056 | 0.291 |
| anxie_t1 ~1 | 3.777 | 3.361 | 4.193 | 4.114 | 17.777 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.326 | 0.152 | 0.501 | 0.968 | 3.669 | 0.000 |
| anxie_t1 ~~ d_anxie_1 | -0.199 | -0.312 | -0.086 | -0.395 | -3.446 | 0.001 |
| d_anxie_1 ~ ppd | 0.113 | 0.023 | 0.203 | 0.180 | 2.450 | 0.014 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.448 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8806.774 | 8896.659 | 0.95 | 0.08 | 0.057 |
# parameters of interest
params_lcs_depre_curr_ppd <- broom::tidy(fit_mi_lcs_depre_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~ ppd", "ppd ~~ ppd", "d_depre_1 ~ ppd", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.573 | NA | NA |
| d_depre_1 ~1 | -0.067 | -0.249 | 0.116 | -0.181 | -0.718 | 0.473 |
| depre_t1 ~1 | 3.796 | 3.484 | 4.108 | 6.055 | 23.861 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.132 | 0.072 | 0.192 | 0.975 | 4.328 | 0.000 |
| depre_t1 ~~ d_depre_1 | -0.044 | -0.079 | -0.010 | -0.209 | -2.550 | 0.011 |
| d_depre_1 ~ ppd | 0.063 | 0.010 | 0.117 | 0.159 | 2.307 | 0.021 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.451 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8937.953 | 9027.839 | 0.984 | 0.042 | 0.031 |
# parameters of interest
params_lcs_volat_curr_ppd <- broom::tidy(fit_mi_lcs_volat_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~ ppd", "ppd ~~ ppd", "d_volat_1 ~ ppd", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.058 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_volat_1 ~1 | 0.020 | -0.269 | 0.308 | 0.038 | 0.135 | 0.893 |
| volat_t1 ~1 | 2.343 | 1.932 | 2.754 | 2.677 | 11.181 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.269 | 0.179 | 0.359 | 0.990 | 5.850 | 0.000 |
| volat_t1 ~~ d_volat_1 | -0.164 | -0.232 | -0.096 | -0.375 | -4.725 | 0.000 |
| d_volat_1 ~ ppd | -0.056 | -0.141 | 0.028 | -0.100 | -1.306 | 0.191 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.973 | 1.000 | 14.446 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8715.505 | 8805.391 | 0.975 | 0.04 | 0.047 |
# parameters of interest
params_lcs_curio_curr_ppd <- broom::tidy(fit_mi_lcs_curio_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~ ppd", "ppd ~~ ppd", "d_curio_1 ~ ppd", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.411 | NA | NA |
| d_curio_1 ~1 | 0.119 | -0.065 | 0.304 | 0.613 | 1.269 | 0.205 |
| curio_t1 ~1 | 3.948 | 3.697 | 4.198 | 8.392 | 30.915 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.038 | -0.009 | 0.084 | 0.994 | 1.593 | 0.111 |
| curio_t1 ~~ d_curio_1 | -0.017 | -0.047 | 0.014 | -0.182 | -1.068 | 0.286 |
| d_curio_1 ~ ppd | -0.016 | -0.071 | 0.038 | -0.077 | -0.586 | 0.558 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.443 | 0.000 |
Results summary (ppd = personal project dimensions):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_ppd <- '
aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1
aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)
aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1
d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2
aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0
aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0
d_aesth_1 ~ 1 # This estimates the intercept of the change score
aesth_t1 ~ 1 # This estimates the intercept of aesth_t1
d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores
aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1
aesth_t1 ~~ d_aesth_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
aesth_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_aesth_1 ~ ppd # This estimates the moderation effect on the change score
bf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2
bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2
bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2
bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2
bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1
bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1
bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1
bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1
bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2
bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2
bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2
bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2
bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1
bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1
bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1
bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2
bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2
bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ppd ~~ ppd
ppd ~ 1
'
fit_mi_lcs_aesth_curr_ppd <- lavaan(mi_lcs_aesth_curr_ppd,
data=df_sbsa_wide_pers_sb_ppd %>% filter(!is.na(bf05_05_t1) & !is.na(bf05_05_t2)),
estimator='mlr', fixed.x=FALSE, missing="fiml")
# This model did not converge properly with missing data and FIML -> no problem when only using complete data
summary(fit_mi_lcs_aesth_curr_ppd, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 197 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 33
Number of equality constraints 10
Number of observations 331
Number of missing patterns 2
Model Test User Model:
Standard Scaled
Test Statistic 31.638 29.022
Degrees of freedom 31 31
P-value (Chi-square) 0.434 0.568
Scaling correction factor 1.090
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1054.713 820.718
Degrees of freedom 36 36
P-value 0.000 0.000
Scaling correction factor 1.285
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.999 1.000
Tucker-Lewis Index (TLI) 0.999 1.003
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.003
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4199.936 -4199.936
Scaling correction factor 0.857
for the MLR correction
Loglikelihood unrestricted model (H1) -4184.117 -4184.117
Scaling correction factor 1.150
for the MLR correction
Akaike (AIC) 8445.872 8445.872
Bayesian (BIC) 8533.321 8533.321
Sample-size adjusted Bayesian (SABIC) 8460.364 8460.364
Root Mean Square Error of Approximation:
RMSEA 0.008 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.042 0.036
P-value H_0: RMSEA <= 0.050 0.987 0.996
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.039
P-value H_0: Robust RMSEA <= 0.050 0.992
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.033 0.033
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t1 =~
b05_05_ 1.000 0.042 0.032
b05_20_ (lmb2) 22.164 1.133 19.565 0.000 0.927 0.835
b05_35_ (lmb3) 20.429 1.125 18.159 0.000 0.854 0.800
b05_50_ (lmb4) -16.841 1.222 -13.783 0.000 -0.704 -0.517
aesth_t2 =~
b05_05_ 1.000 0.043 0.032
b05_20_ (lmb2) 22.164 1.133 19.565 0.000 0.951 0.842
b05_35_ (lmb3) 20.429 1.125 18.159 0.000 0.876 0.808
b05_50_ (lmb4) -16.841 1.222 -13.783 0.000 -0.722 -0.527
d_aesth_1 =~
asth_t2 1.000 0.584 0.584
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
aesth_t2 ~
aesth_t1 1.000 0.975 0.975
aesth_t1 ~
ppd 0.003 0.003 1.153 0.249 0.073 0.068
d_aesth_1 ~
ppd -0.003 0.002 -1.250 0.211 -0.114 -0.106
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t1 ~~
.d_aesth_1 -0.000 0.000 -2.609 0.009 -0.251 -0.251
.bf05_05_t1 ~~
.bf05_05_t2 0.550 0.106 5.212 0.000 0.550 0.316
.bf05_20_t1 ~~
.bf05_20_t2 0.103 0.070 1.474 0.140 0.103 0.278
.bf05_35_t1 ~~
.bf05_35_t2 0.111 0.061 1.830 0.067 0.111 0.272
.bf05_50_t1 ~~
.bf05_50_t2 0.821 0.098 8.356 0.000 0.821 0.604
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.aesth_t2 0.000 0.000 0.000
.d_aesth_1 0.012 0.008 1.478 0.139 0.462 0.462
.aesth_t1 2.967 0.059 49.966 0.000 70.950 70.950
.bf05_05_1 0.000 0.000 0.000
.bf05_20_1 (m2) -62.174 3.176 -19.578 0.000 -62.174 -56.036
.bf05_35_1 (m3) -56.995 3.060 -18.625 0.000 -56.995 -53.392
.bf05_50_1 (m4) 52.719 3.631 14.521 0.000 52.719 38.702
.bf05_05_2 0.000 0.000 0.000
.bf05_20_2 (m2) -62.174 3.176 -19.578 0.000 -62.174 -55.048
.bf05_35_2 (m3) -56.995 3.060 -18.625 0.000 -56.995 -52.527
.bf05_50_2 (m4) 52.719 3.631 14.521 0.000 52.719 38.436
ppd 3.455 0.051 67.142 0.000 3.455 3.713
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.asth_t2 0.000 0.000 0.000
.d_sth_1 0.001 0.000 3.919 0.000 0.989 0.989
.asth_t1 0.002 0.000 9.808 0.000 0.995 0.995
.b05_05_ (res1) 1.742 0.070 24.770 0.000 1.742 0.999
.b05_20_ (res2) 0.372 0.077 4.842 0.000 0.372 0.302
.b05_35_ (res3) 0.409 0.059 6.972 0.000 0.409 0.359
.b05_50_ (res4) 1.359 0.099 13.767 0.000 1.359 0.733
.b05_05_ (res1) 1.742 0.070 24.770 0.000 1.742 0.999
.b05_20_ (res2) 0.372 0.077 4.842 0.000 0.372 0.291
.b05_35_ (res3) 0.409 0.059 6.972 0.000 0.409 0.348
.b05_50_ (res4) 1.359 0.099 13.767 0.000 1.359 0.723
ppd 0.865 0.060 14.460 0.000 0.865 1.000# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 331 | 23 | 8445.872 | 8533.321 | 0.999 | 0.008 | 0.033 |
# parameters of interest
params_lcs_aesth_curr_ppd <- broom::tidy(fit_mi_lcs_aesth_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~ ppd", "ppd ~~ ppd", "d_aesth_1 ~ ppd", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.584 | NA | NA |
| d_aesth_1 ~1 | 0.012 | -0.004 | 0.027 | 0.462 | 1.478 | 0.139 |
| aesth_t1 ~1 | 2.967 | 2.851 | 3.084 | 70.950 | 49.966 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.989 | 3.919 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | -0.251 | -2.609 | 0.009 |
| d_aesth_1 ~ ppd | -0.003 | -0.007 | 0.002 | -0.106 | -1.250 | 0.211 |
| ppd ~~ ppd | 0.865 | 0.748 | 0.983 | 1.000 | 14.460 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 368 | 23 | 8514.815 | 8604.701 | 0.956 | 0.061 | 0.059 |
# parameters of interest
params_lcs_imagi_curr_ppd <- broom::tidy(fit_mi_lcs_imagi_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~ ppd", "ppd ~~ ppd", "d_imagi_1 ~ ppd", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.473 | NA | NA |
| d_imagi_1 ~1 | -0.031 | -0.263 | 0.201 | -0.088 | -0.264 | 0.792 |
| imagi_t1 ~1 | 3.887 | 3.553 | 4.222 | 5.359 | 22.792 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.124 | 0.042 | 0.206 | 0.990 | 2.950 | 0.003 |
| imagi_t1 ~~ d_imagi_1 | -0.042 | -0.109 | 0.024 | -0.167 | -1.245 | 0.213 |
| d_imagi_1 ~ ppd | 0.038 | -0.029 | 0.105 | 0.099 | 1.106 | 0.269 |
| ppd ~~ ppd | 0.857 | 0.741 | 0.974 | 1.000 | 14.441 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_ppd_improve <- bind_rows(
#traits
params_lcs_extra_curr_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_curr_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_curr_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_curr_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_curr_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_curr_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_curr_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_curr_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_curr_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_curr_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_curr_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_curr_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_curr_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_curr_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_curr_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_curr_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_curr_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_curr_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_curr_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_curr_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20)) %>%
select(trait, moderator, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_improve[1:5, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| extraversion | ppd | 0.065 | 0.170 | 2.364 | 0.018 |
| agreeableness | ppd | -0.009 | -0.037 | -0.490 | 0.624 |
| conscientiousness | ppd | -0.010 | -0.025 | -0.400 | 0.689 |
| neuroticism | ppd | -0.028 | -0.052 | -0.884 | 0.376 |
| openness | ppd | -0.003 | -0.014 | -0.152 | 0.879 |
Results summary across the Big Five facets: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_improve[6:20, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| sociability | ppd | 0.080 | 0.141 | 1.743 | 0.081 |
| assertiveness | ppd | 0.064 | 0.243 | 2.094 | 0.036 |
| energy | ppd | -0.019 | -0.077 | -0.852 | 0.394 |
| compassion | ppd | 0.041 | 0.151 | 1.267 | 0.205 |
| respectfulness | ppd | -0.033 | -0.093 | -1.353 | 0.176 |
| trust | ppd | 0.010 | 0.027 | 0.311 | 0.756 |
| organization | ppd | -0.039 | -0.070 | -1.011 | 0.312 |
| productiveness | ppd | 0.000 | 0.000 | -0.002 | 0.999 |
| responsibility | ppd | -0.049 | -0.217 | -2.068 | 0.039 |
| anxiety | ppd | 0.043 | 0.069 | 1.033 | 0.302 |
| depression | ppd | 0.032 | 0.081 | 1.089 | 0.276 |
| volatility | ppd | 0.003 | 0.005 | 0.075 | 0.941 |
| curiosity | ppd | -0.014 | -0.065 | -0.506 | 0.613 |
| aesthetic | ppd | -0.002 | -0.089 | -1.109 | 0.268 |
| imagination | ppd | 0.030 | 0.079 | 0.877 | 0.380 |
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sa_ppd <- df_sbsa %>%
filter(rando=="Self-Acceptance") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa01"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sa01")))) %>%
select(-c(sa01_01_t2, sa01_02_t2))
# colnames(df_sbsa_wide_pers_sa_ppd)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sa_ppd)
# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)
df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers_sa_ppd %>% mutate(ppd = rowMeans(across(c(sa01_01_t1, sa01_02_t1)), na.rm=T))Run models for all traits with a template & loop:
# create templates
trait_template_ppd_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~~ d_trait_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
trait_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_trait_1 ~ ppd # This estimates the moderation effect on the change score
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_ppd_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), trait_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 19 | 3928.928 | 4002.066 | 0.988 | 0.034 | 0.036 |
# parameters of interest
params_lcs_extra_ideal_ppd <- broom::tidy(fit_mi_lcs_extra_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "extra_t1 ~~ d_extra_1", # change parameters
"d_extra_1 ~ ppd", "ppd ~~ ppd", "extra_t1 ~ ppd", # acceptance ppd
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.902 | NA | NA |
| d_extra_1 ~1 | -0.174 | -0.460 | 0.112 | -0.443 | -1.194 | 0.233 |
| extra_t1 ~1 | 3.849 | 3.546 | 4.152 | 8.243 | 24.875 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.154 | 0.084 | 0.224 | 0.995 | 4.311 | 0.000 |
| extra_t1 ~~ d_extra_1 | -0.090 | -0.138 | -0.043 | -0.494 | -3.716 | 0.000 |
| extra_t1 ~ ppd | -0.036 | -0.114 | 0.043 | -0.059 | -0.895 | 0.371 |
| d_extra_1 ~ ppd | 0.036 | -0.041 | 0.113 | 0.070 | 0.913 | 0.361 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.735 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 19 | 3938.359 | 4011.496 | 1 | 0 | 0.029 |
# parameters of interest
params_lcs_agree_ideal_ppd <- broom::tidy(fit_mi_lcs_agree_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "agree_t1 ~~ d_agree_1", # change parameters
"d_agree_1 ~ ppd", "ppd ~~ ppd", "agree_t1 ~ ppd", # acceptance ppd
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.672 | NA | NA |
| d_agree_1 ~1 | -0.063 | -0.294 | 0.169 | -0.197 | -0.532 | 0.595 |
| agree_t1 ~1 | 3.389 | 3.062 | 3.716 | 7.266 | 20.324 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.101 | 0.061 | 0.141 | 0.999 | 4.961 | 0.000 |
| agree_t1 ~~ d_agree_1 | -0.048 | -0.078 | -0.018 | -0.328 | -3.171 | 0.002 |
| agree_t1 ~ ppd | 0.083 | 0.001 | 0.166 | 0.137 | 1.982 | 0.047 |
| d_agree_1 ~ ppd | 0.013 | -0.046 | 0.072 | 0.032 | 0.441 | 0.659 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 19 | 3540.684 | 3613.821 | 0.991 | 0.037 | 0.039 |
# parameters of interest
params_lcs_consc_ideal_ppd <- broom::tidy(fit_mi_lcs_consc_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "consc_t1 ~~ d_consc_1", # change parameters
"d_consc_1 ~ ppd", "ppd ~~ ppd", "consc_t1 ~ ppd", # acceptance ppd
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.046 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.739 | NA | NA |
| d_consc_1 ~1 | 0.248 | 0.014 | 0.483 | 0.714 | 2.074 | 0.038 |
| consc_t1 ~1 | 4.048 | 3.762 | 4.334 | 8.218 | 27.752 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.119 | 0.058 | 0.179 | 0.982 | 3.853 | 0.000 |
| consc_t1 ~~ d_consc_1 | -0.068 | -0.129 | -0.007 | -0.403 | -2.185 | 0.029 |
| consc_t1 ~ ppd | 0.087 | 0.015 | 0.159 | 0.135 | 2.364 | 0.018 |
| d_consc_1 ~ ppd | -0.061 | -0.120 | -0.001 | -0.134 | -2.001 | 0.045 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.738 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 19 | 3633.173 | 3706.31 | 0.972 | 0.055 | 0.05 |
# parameters of interest
params_lcs_neuro_ideal_ppd <- broom::tidy(fit_mi_lcs_neuro_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "neuro_t1 ~~ d_neuro_1", # change parameters
"d_neuro_1 ~ ppd", "ppd ~~ ppd", "neuro_t1 ~ ppd", # acceptance ppd
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.937 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.780 | NA | NA |
| d_neuro_1 ~1 | -0.017 | -0.250 | 0.216 | -0.053 | -0.144 | 0.885 |
| neuro_t1 ~1 | 1.751 | 1.499 | 2.004 | 4.490 | 13.583 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.105 | 0.055 | 0.155 | 1.000 | 4.139 | 0.000 |
| neuro_t1 ~~ d_neuro_1 | -0.042 | -0.071 | -0.013 | -0.333 | -2.864 | 0.004 |
| neuro_t1 ~ ppd | -0.028 | -0.092 | 0.037 | -0.055 | -0.843 | 0.399 |
| d_neuro_1 ~ ppd | 0.007 | -0.054 | 0.069 | 0.017 | 0.237 | 0.813 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 19 | 3936.496 | 4009.633 | 0.98 | 0.05 | 0.037 |
# parameters of interest
params_lcs_openn_ideal_ppd <- broom::tidy(fit_mi_lcs_openn_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "openn_t1 ~~ d_openn_1", # change parameters
"d_openn_1 ~ ppd", "ppd ~~ ppd", "openn_t1 ~ ppd", # acceptance ppd
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.967 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.638 | NA | NA |
| d_openn_1 ~1 | -0.029 | -0.214 | 0.157 | -0.122 | -0.304 | 0.761 |
| openn_t1 ~1 | 3.689 | 3.484 | 3.894 | 10.320 | 35.221 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.056 | 0.028 | 0.083 | 1.000 | 4.008 | 0.000 |
| openn_t1 ~~ d_openn_1 | -0.023 | -0.040 | -0.007 | -0.278 | -2.844 | 0.004 |
| openn_t1 ~ ppd | 0.032 | -0.020 | 0.084 | 0.069 | 1.218 | 0.223 |
| d_openn_1 ~ ppd | 0.004 | -0.044 | 0.052 | 0.013 | 0.167 | 0.868 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goal
facet_template_ppd_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~~ d_facet_1 # This estimates the self-feedback parameter, as a covariance! -> therefore, the interpretation of the change score
facet_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_facet_1 ~ ppd # This estimates the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[6]], item_nrs)
template_filled <- str_replace_all(facet_template_ppd_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), facet_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7931.159 | 8019.694 | 0.961 | 0.044 | 0.048 |
# parameters of interest
params_lcs_socia_ideal_ppd <- broom::tidy(fit_mi_lcs_socia_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "socia_t1 ~~ d_socia_1", # change parameters
"d_socia_1 ~ ppd", "ppd ~~ ppd", "d_socia_1 ~ ppd", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.049 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.747 | NA | NA |
| d_socia_1 ~1 | -0.051 | -0.291 | 0.190 | -0.200 | -0.412 | 0.681 |
| socia_t1 ~1 | 4.408 | 4.155 | 4.660 | 12.439 | 34.233 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.064 | 0.018 | 0.110 | 1.000 | 2.714 | 0.007 |
| socia_t1 ~~ d_socia_1 | -0.038 | -0.063 | -0.012 | -0.421 | -2.935 | 0.003 |
| d_socia_1 ~ ppd | -0.002 | -0.066 | 0.062 | -0.006 | -0.058 | 0.954 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.732 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8187.854 | 8276.388 | 0.989 | 0.022 | 0.034 |
# parameters of interest
params_lcs_asser_ideal_ppd <- broom::tidy(fit_mi_lcs_asser_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "asser_t1 ~~ d_asser_1", # change parameters
"d_asser_1 ~ ppd", "ppd ~~ ppd", "d_asser_1 ~ ppd", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.749 | NA | NA |
| d_asser_1 ~1 | -0.146 | -0.326 | 0.033 | -0.744 | -1.597 | 0.110 |
| asser_t1 ~1 | 4.312 | 4.109 | 4.516 | 16.208 | 41.555 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.038 | 0.001 | 0.075 | 0.987 | 2.031 | 0.042 |
| asser_t1 ~~ d_asser_1 | -0.020 | -0.042 | 0.003 | -0.378 | -1.733 | 0.083 |
| d_asser_1 ~ ppd | 0.030 | -0.018 | 0.078 | 0.116 | 1.213 | 0.225 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.736 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7549.389 | 7637.923 | 0.911 | 0.05 | 0.06 |
# parameters of interest
params_lcs_energ_ideal_ppd <- broom::tidy(fit_mi_lcs_energ_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "energ_t1 ~~ d_energ_1", # change parameters
"d_energ_1 ~ ppd", "ppd ~~ ppd", "d_energ_1 ~ ppd", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.105 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.930 | NA | NA |
| d_energ_1 ~1 | -0.010 | -0.210 | 0.190 | -0.041 | -0.095 | 0.925 |
| energ_t1 ~1 | 2.485 | 2.252 | 2.717 | 8.916 | 20.961 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.055 | -0.032 | 0.142 | 1.000 | 1.234 | 0.217 |
| energ_t1 ~~ d_energ_1 | -0.035 | -0.096 | 0.027 | -0.530 | -1.095 | 0.274 |
| d_energ_1 ~ ppd | -0.001 | -0.050 | 0.048 | -0.002 | -0.025 | 0.980 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.738 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8569.885 | 8658.42 | 1 | 0 | 0.034 |
# parameters of interest
params_lcs_compa_ideal_ppd <- broom::tidy(fit_mi_lcs_compa_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "compa_t1 ~~ d_compa_1", # change parameters
"d_compa_1 ~ ppd", "ppd ~~ ppd", "d_compa_1 ~ ppd", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.580 | NA | NA |
| d_compa_1 ~1 | 0.007 | -0.403 | 0.418 | 0.021 | 0.034 | 0.973 |
| compa_t1 ~1 | 3.805 | 3.274 | 4.336 | 6.474 | 14.037 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.113 | 0.003 | 0.223 | 0.999 | 2.005 | 0.045 |
| compa_t1 ~~ d_compa_1 | -0.060 | -0.132 | 0.011 | -0.310 | -1.665 | 0.096 |
| d_compa_1 ~ ppd | -0.013 | -0.121 | 0.096 | -0.029 | -0.226 | 0.821 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.742 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 6847.15 | 6935.684 | 0.932 | 0.059 | 0.057 |
# parameters of interest
params_lcs_respe_ideal_ppd <- broom::tidy(fit_mi_lcs_respe_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "respe_t1 ~~ d_respe_1", # change parameters
"d_respe_1 ~ ppd", "ppd ~~ ppd", "d_respe_1 ~ ppd", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.951 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.661 | NA | NA |
| d_respe_1 ~1 | -0.057 | -0.283 | 0.168 | -0.242 | -0.496 | 0.620 |
| respe_t1 ~1 | 4.469 | 4.214 | 4.724 | 13.133 | 34.329 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.056 | 0.013 | 0.099 | 0.997 | 2.546 | 0.011 |
| respe_t1 ~~ d_respe_1 | -0.022 | -0.054 | 0.010 | -0.280 | -1.370 | 0.171 |
| d_respe_1 ~ ppd | 0.017 | -0.040 | 0.073 | 0.054 | 0.583 | 0.560 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.736 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8175.243 | 8263.778 | 0.952 | 0.049 | 0.043 |
# parameters of interest
params_lcs_trust_ideal_ppd <- broom::tidy(fit_mi_lcs_trust_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "trust_t1 ~~ d_trust_1", # change parameters
"d_trust_1 ~ ppd", "ppd ~~ ppd", "d_trust_1 ~ ppd", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.642 | NA | NA |
| d_trust_1 ~1 | -0.028 | -0.305 | 0.250 | -0.097 | -0.195 | 0.846 |
| trust_t1 ~1 | 2.133 | 1.786 | 2.481 | 4.893 | 12.034 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.081 | 0.030 | 0.132 | 0.998 | 3.114 | 0.002 |
| trust_t1 ~~ d_trust_1 | -0.037 | -0.073 | -0.001 | -0.298 | -2.012 | 0.044 |
| d_trust_1 ~ ppd | 0.015 | -0.057 | 0.087 | 0.041 | 0.408 | 0.683 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.734 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7046.635 | 7135.17 | 0.951 | 0.045 | 0.048 |
# parameters of interest
params_lcs_organ_ideal_ppd <- broom::tidy(fit_mi_lcs_organ_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "organ_t1 ~~ d_organ_1", # change parameters
"d_organ_1 ~ ppd", "ppd ~~ ppd", "d_organ_1 ~ ppd", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.827 | NA | NA |
| d_organ_1 ~1 | -0.132 | -0.296 | 0.032 | -0.656 | -1.576 | 0.115 |
| organ_t1 ~1 | 1.898 | 1.715 | 2.082 | 7.523 | 20.284 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.040 | 0.002 | 0.077 | 0.982 | 2.067 | 0.039 |
| organ_t1 ~~ d_organ_1 | -0.021 | -0.041 | -0.002 | -0.431 | -2.121 | 0.034 |
| d_organ_1 ~ ppd | 0.035 | -0.008 | 0.078 | 0.134 | 1.612 | 0.107 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.739 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 6553.056 | 6641.59 | 0.883 | 0.067 | 0.065 |
# parameters of interest
params_lcs_produ_ideal_ppd <- broom::tidy(fit_mi_lcs_produ_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "produ_t1 ~~ d_produ_1", # change parameters
"d_produ_1 ~ ppd", "ppd ~~ ppd", "d_produ_1 ~ ppd", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.083 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.740 | NA | NA |
| d_produ_1 ~1 | -0.152 | -0.382 | 0.079 | -0.559 | -1.290 | 0.197 |
| produ_t1 ~1 | 1.712 | 1.458 | 1.967 | 4.308 | 13.178 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.073 | 0.013 | 0.133 | 0.991 | 2.375 | 0.018 |
| produ_t1 ~~ d_produ_1 | -0.048 | -0.097 | 0.001 | -0.447 | -1.908 | 0.056 |
| d_produ_1 ~ ppd | 0.033 | -0.027 | 0.093 | 0.093 | 1.069 | 0.285 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7520.311 | 7608.845 | 0.987 | 0.024 | 0.042 |
# parameters of interest
params_lcs_respo_ideal_ppd <- broom::tidy(fit_mi_lcs_respo_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "respo_t1 ~~ d_respo_1", # change parameters
"d_respo_1 ~ ppd", "ppd ~~ ppd", "d_respo_1 ~ ppd", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.054 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.789 | NA | NA |
| d_respo_1 ~1 | 0.252 | -0.054 | 0.558 | 0.666 | 1.616 | 0.106 |
| respo_t1 ~1 | 3.954 | 3.574 | 4.334 | 7.820 | 20.374 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.141 | 0.036 | 0.247 | 0.986 | 2.622 | 0.009 |
| respo_t1 ~~ d_respo_1 | -0.081 | -0.150 | -0.013 | -0.432 | -2.344 | 0.019 |
| d_respo_1 ~ ppd | -0.058 | -0.136 | 0.019 | -0.118 | -1.472 | 0.141 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8132.814 | 8221.348 | 0.896 | 0.047 | 0.049 |
# parameters of interest
params_lcs_anxie_ideal_ppd <- broom::tidy(fit_mi_lcs_anxie_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "anxie_t1 ~~ d_anxie_1", # change parameters
"d_anxie_1 ~ ppd", "ppd ~~ ppd", "d_anxie_1 ~ ppd", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.854 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.769 | NA | NA |
| d_anxie_1 ~1 | 0.029 | -0.384 | 0.442 | 0.110 | 0.138 | 0.890 |
| anxie_t1 ~1 | 4.426 | 4.103 | 4.749 | 15.044 | 26.860 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.070 | -0.044 | 0.184 | 0.997 | 1.200 | 0.230 |
| anxie_t1 ~~ d_anxie_1 | -0.019 | -0.060 | 0.023 | -0.240 | -0.884 | 0.377 |
| d_anxie_1 ~ ppd | -0.020 | -0.134 | 0.094 | -0.058 | -0.344 | 0.731 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.734 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 6585.959 | 6674.494 | 0.821 | 0.084 | 0.074 |
# parameters of interest
params_lcs_depre_ideal_ppd <- broom::tidy(fit_mi_lcs_depre_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "depre_t1 ~~ d_depre_1", # change parameters
"d_depre_1 ~ ppd", "ppd ~~ ppd", "d_depre_1 ~ ppd", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.063 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.812 | NA | NA |
| d_depre_1 ~1 | -0.016 | -0.190 | 0.158 | -0.072 | -0.181 | 0.857 |
| depre_t1 ~1 | 4.411 | 4.220 | 4.602 | 15.111 | 45.242 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.050 | 0.003 | 0.096 | 0.999 | 2.100 | 0.036 |
| depre_t1 ~~ d_depre_1 | -0.030 | -0.059 | -0.001 | -0.457 | -2.018 | 0.044 |
| d_depre_1 ~ ppd | 0.009 | -0.036 | 0.054 | 0.031 | 0.389 | 0.697 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7638.671 | 7727.205 | 0.955 | 0.038 | 0.046 |
# parameters of interest
params_lcs_volat_ideal_ppd <- broom::tidy(fit_mi_lcs_volat_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "volat_t1 ~~ d_volat_1", # change parameters
"d_volat_1 ~ ppd", "ppd ~~ ppd", "d_volat_1 ~ ppd", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.913 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_volat_1 ~1 | 0.057 | -0.266 | 0.379 | 0.179 | 0.345 | 0.730 |
| volat_t1 ~1 | 1.745 | 1.416 | 2.074 | 4.033 | 10.396 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.100 | 0.021 | 0.179 | 1.000 | 2.485 | 0.013 |
| volat_t1 ~~ d_volat_1 | -0.032 | -0.085 | 0.021 | -0.231 | -1.171 | 0.241 |
| d_volat_1 ~ ppd | -0.009 | -0.094 | 0.076 | -0.022 | -0.209 | 0.834 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.738 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8077.27 | 8165.805 | 0.988 | 0.02 | 0.04 |
# parameters of interest
params_lcs_curio_ideal_ppd <- broom::tidy(fit_mi_lcs_curio_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "curio_t1 ~~ d_curio_1", # change parameters
"d_curio_1 ~ ppd", "ppd ~~ ppd", "d_curio_1 ~ ppd", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.043 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.683 | NA | NA |
| d_curio_1 ~1 | -0.082 | -0.316 | 0.152 | -0.407 | -0.684 | 0.494 |
| curio_t1 ~1 | 4.478 | 4.241 | 4.716 | 14.590 | 36.967 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.040 | -0.008 | 0.088 | 0.991 | 1.647 | 0.100 |
| curio_t1 ~~ d_curio_1 | -0.024 | -0.050 | 0.002 | -0.392 | -1.833 | 0.067 |
| d_curio_1 ~ ppd | 0.025 | -0.036 | 0.086 | 0.095 | 0.800 | 0.424 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.738 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 8127.044 | 8215.579 | 0.987 | 0.031 | 0.039 |
# parameters of interest
params_lcs_aesth_ideal_ppd <- broom::tidy(fit_mi_lcs_aesth_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "aesth_t1 ~~ d_aesth_1", # change parameters
"d_aesth_1 ~ ppd", "ppd ~~ ppd", "d_aesth_1 ~ ppd", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.947 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_aesth_1 ~1 | -0.002 | -0.018 | 0.014 | -0.089 | -0.258 | 0.796 |
| aesth_t1 ~1 | 3.180 | 3.051 | 3.309 | 93.349 | 48.188 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 1.000 | 4.215 | 0.000 |
| aesth_t1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | -0.277 | -1.794 | 0.073 |
| d_aesth_1 ~ ppd | 0.000 | -0.004 | 0.004 | 0.008 | 0.124 | 0.901 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.743 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 347 | 23 | 7475.448 | 7563.982 | 0.934 | 0.047 | 0.054 |
# parameters of interest
params_lcs_imagi_ideal_ppd <- broom::tidy(fit_mi_lcs_imagi_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "imagi_t1 ~~ d_imagi_1", # change parameters
"d_imagi_1 ~ ppd", "ppd ~~ ppd", "d_imagi_1 ~ ppd", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.874 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.550 | NA | NA |
| d_imagi_1 ~1 | 0.116 | -0.126 | 0.359 | 0.561 | 0.941 | 0.347 |
| imagi_t1 ~1 | 4.535 | 4.300 | 4.771 | 13.754 | 37.732 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.042 | 0.000 | 0.084 | 0.976 | 1.958 | 0.050 |
| imagi_t1 ~~ d_imagi_1 | -0.004 | -0.034 | 0.026 | -0.062 | -0.278 | 0.781 |
| d_imagi_1 ~ ppd | -0.042 | -0.106 | 0.021 | -0.156 | -1.305 | 0.192 |
| ppd ~~ ppd | 0.589 | 0.505 | 0.673 | 1.000 | 13.737 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_ppd_accept <- bind_rows(
#traits
params_lcs_extra_ideal_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_ideal_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_ideal_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_ideal_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_ideal_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_ideal_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_ideal_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_ideal_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_ideal_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_ideal_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_ideal_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_ideal_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_ideal_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_ideal_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_ideal_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_ideal_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_ideal_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_ideal_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_ideal_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_ideal_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20)) %>%
select(trait, moderator, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_accept[1:5, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| extraversion | ppd | 0.021 | 0.041 | 0.628 | 0.530 |
| agreeableness | ppd | 0.032 | 0.077 | 1.198 | 0.231 |
| conscientiousness | ppd | -0.036 | -0.079 | -1.392 | 0.164 |
| neuroticism | ppd | 0.000 | -0.001 | -0.011 | 0.991 |
| openness | ppd | 0.010 | 0.032 | 0.445 | 0.656 |
Results summary across the Big Five facets: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_accept[6:20, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| sociability | ppd | -0.009 | -0.027 | -0.305 | 0.760 |
| assertiveness | ppd | 0.019 | 0.073 | 0.837 | 0.403 |
| energy | ppd | -0.012 | -0.038 | -0.557 | 0.577 |
| compassion | ppd | 0.009 | 0.020 | 0.175 | 0.861 |
| respectfulness | ppd | 0.027 | 0.088 | 1.093 | 0.274 |
| trust | ppd | 0.003 | 0.009 | 0.096 | 0.923 |
| organization | ppd | 0.017 | 0.063 | 0.877 | 0.380 |
| productiveness | ppd | 0.025 | 0.071 | 0.925 | 0.355 |
| responsibility | ppd | -0.031 | -0.063 | -0.870 | 0.384 |
| anxiety | ppd | -0.012 | -0.034 | -0.222 | 0.824 |
| depression | ppd | 0.009 | 0.030 | 0.441 | 0.660 |
| volatility | ppd | -0.011 | -0.026 | -0.263 | 0.793 |
| curiosity | ppd | 0.026 | 0.099 | 0.941 | 0.347 |
| aesthetic | ppd | 0.002 | 0.056 | 0.808 | 0.419 |
| imagination | ppd | -0.041 | -0.153 | -1.318 | 0.187 |
Prepare data frame for plotting:
df_table_ppd_plot1 <- bind_rows(
#traits
params_lcs_extra_curr_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_curr_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_curr_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_curr_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_curr_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_curr_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_curr_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_curr_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_curr_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_curr_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_curr_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_curr_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_curr_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_curr_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_curr_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_curr_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_curr_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_curr_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_curr_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_curr_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20),
ref = "self-improvement") %>%
select(trait, moderator, ref, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_ppd_plot2 <- bind_rows(
#traits
params_lcs_extra_ideal_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_ideal_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_ideal_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_ideal_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_ideal_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_ideal_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_ideal_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_ideal_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_ideal_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_ideal_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_ideal_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_ideal_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_ideal_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_ideal_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_ideal_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_ideal_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_ideal_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_ideal_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_ideal_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_ideal_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20),
ref = "self-acceptance") %>%
select(trait, moderator, ref, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_ppd_plot <- bind_rows(df_table_ppd_plot1, df_table_ppd_plot2)
df_table_ppd_plot <- df_table_ppd_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref2 = factor(ref, levels = c("self-improvement", "self-acceptance"), labels = c("self-improvement", "self-acceptance"))) %>%
mutate(moderator = factor("personal project dimensions", levels = c("personal project dimensions"), labels = c("personal project dimensions"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_ppd_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ moderator + fct_rev(ref), ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
We will explore c) whether there are stronger rates of change/acceptance on traits that were selected as change goals than those that were not.
TBD!
Does self-improvement intervene on current self trait change (but not ideal self)? Does self-acceptance intervene on ideal self trait change (but not current self)?
Changes in current self traits over time:
df_manip_check_curr <- df_sbsa %>%
select(pid, time_d, rando,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_curr")) %>%
pivot_longer(ends_with("_comb_curr"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_manip_check_curr_unlist <- as.data.frame(summary(df_manip_check_curr[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(df_manip_check_curr)) {
df_manip_check_curr_unlist <- bind_rows(df_manip_check_curr_unlist,
as.data.frame(summary(df_manip_check_curr[[i]])$coefficients) %>% as_tibble())
}kable(df_manip_check_curr_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
rename(p = `Pr(>|t|)`, str_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, str_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | est | str_err | p | sig |
|---|---|---|---|---|---|
| extraversion | Intercept | 2.796 | 0.038 | 0.000 | *** |
| extraversion | time | 0.166 | 0.026 | 0.000 | *** |
| extraversion | group | 0.001 | 0.053 | 0.984 | |
| extraversion | time*group | -0.026 | 0.037 | 0.488 | |
| agreeableness | Intercept | 3.553 | 0.032 | 0.000 | *** |
| agreeableness | time | 0.076 | 0.024 | 0.002 | ** |
| agreeableness | group | 0.016 | 0.045 | 0.724 | |
| agreeableness | time*group | 0.018 | 0.034 | 0.594 | |
| conscientiousness | Intercept | 3.328 | 0.041 | 0.000 | *** |
| conscientiousness | time | 0.151 | 0.024 | 0.000 | *** |
| conscientiousness | group | 0.056 | 0.058 | 0.333 | |
| conscientiousness | time*group | -0.037 | 0.034 | 0.282 | |
| neuroticism | Intercept | 3.306 | 0.045 | 0.000 | *** |
| neuroticism | time | -0.192 | 0.031 | 0.000 | *** |
| neuroticism | group | -0.023 | 0.063 | 0.717 | |
| neuroticism | time*group | -0.020 | 0.044 | 0.645 | |
| openness | Intercept | 3.654 | 0.033 | 0.000 | *** |
| openness | time | 0.082 | 0.023 | 0.000 | *** |
| openness | group | -0.048 | 0.046 | 0.293 | |
| openness | time*group | -0.003 | 0.032 | 0.934 | |
| sociability | Intercept | 2.469 | 0.051 | 0.000 | *** |
| sociability | time | 0.191 | 0.036 | 0.000 | *** |
| sociability | group | -0.023 | 0.072 | 0.753 | |
| sociability | time*group | -0.007 | 0.050 | 0.893 | |
| assertiveness | Intercept | 2.855 | 0.046 | 0.000 | *** |
| assertiveness | time | 0.122 | 0.035 | 0.000 | *** |
| assertiveness | group | 0.024 | 0.065 | 0.713 | |
| assertiveness | time*group | -0.044 | 0.048 | 0.365 | |
| energy | Intercept | 3.063 | 0.046 | 0.000 | *** |
| energy | time | 0.188 | 0.039 | 0.000 | *** |
| energy | group | 0.002 | 0.064 | 0.980 | |
| energy | time*group | -0.027 | 0.054 | 0.617 | |
| compassion | Intercept | 3.670 | 0.039 | 0.000 | *** |
| compassion | time | 0.052 | 0.036 | 0.146 | |
| compassion | group | 0.031 | 0.055 | 0.567 | |
| compassion | time*group | 0.013 | 0.050 | 0.790 | |
| respectfulness | Intercept | 3.953 | 0.040 | 0.000 | *** |
| respectfulness | time | 0.075 | 0.032 | 0.022 | * |
| respectfulness | group | -0.029 | 0.056 | 0.603 | |
| respectfulness | time*group | 0.005 | 0.045 | 0.905 | |
| trust | Intercept | 3.035 | 0.044 | 0.000 | *** |
| trust | time | 0.100 | 0.035 | 0.005 | ** |
| trust | group | 0.043 | 0.061 | 0.481 | |
| trust | time*group | 0.039 | 0.049 | 0.433 | |
| organization | Intercept | 3.431 | 0.054 | 0.000 | *** |
| organization | time | 0.125 | 0.036 | 0.001 | *** |
| organization | group | 0.061 | 0.076 | 0.419 | |
| organization | time*group | -0.005 | 0.050 | 0.924 | |
| productiveness | Intercept | 3.095 | 0.049 | 0.000 | *** |
| productiveness | time | 0.197 | 0.033 | 0.000 | *** |
| productiveness | group | 0.067 | 0.068 | 0.325 | |
| productiveness | time*group | -0.045 | 0.047 | 0.340 | |
| responsibility | Intercept | 3.456 | 0.042 | 0.000 | *** |
| responsibility | time | 0.128 | 0.032 | 0.000 | *** |
| responsibility | group | 0.041 | 0.059 | 0.487 | |
| responsibility | time*group | -0.056 | 0.045 | 0.206 | |
| anxiety | Intercept | 3.608 | 0.048 | 0.000 | *** |
| anxiety | time | -0.147 | 0.039 | 0.000 | *** |
| anxiety | group | -0.022 | 0.067 | 0.741 | |
| anxiety | time*group | -0.063 | 0.055 | 0.251 | |
| depression | Intercept | 3.221 | 0.054 | 0.000 | *** |
| depression | time | -0.280 | 0.041 | 0.000 | *** |
| depression | group | -0.030 | 0.076 | 0.691 | |
| depression | time*group | 0.035 | 0.057 | 0.535 | |
| volatility | Intercept | 3.088 | 0.054 | 0.000 | *** |
| volatility | time | -0.147 | 0.042 | 0.000 | *** |
| volatility | group | -0.015 | 0.075 | 0.837 | |
| volatility | time*group | -0.036 | 0.058 | 0.538 | |
| curiosity | Intercept | 3.771 | 0.040 | 0.000 | *** |
| curiosity | time | 0.114 | 0.034 | 0.001 | *** |
| curiosity | group | -0.036 | 0.056 | 0.527 | |
| curiosity | time*group | -0.038 | 0.047 | 0.425 | |
| aesthetic | Intercept | 3.574 | 0.044 | 0.000 | *** |
| aesthetic | time | 0.045 | 0.034 | 0.192 | |
| aesthetic | group | -0.070 | 0.061 | 0.247 | |
| aesthetic | time*group | 0.017 | 0.048 | 0.730 | |
| imagination | Intercept | 3.618 | 0.043 | 0.000 | *** |
| imagination | time | 0.089 | 0.033 | 0.008 | ** |
| imagination | group | -0.039 | 0.061 | 0.524 | |
| imagination | time*group | 0.011 | 0.047 | 0.817 |
Changes in ideal self traits over time:
df_manip_check_ideal <- df_sbsa %>%
select(pid, time_d, rando,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_ideal")) %>%
pivot_longer(ends_with("_comb_ideal"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_manip_check_ideal_unlist <- as.data.frame(summary(df_manip_check_ideal[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(df_manip_check_ideal)) {
df_manip_check_ideal_unlist <- bind_rows(df_manip_check_ideal_unlist,
as.data.frame(summary(df_manip_check_ideal[[i]])$coefficients) %>% as_tibble())
}kable(df_manip_check_ideal_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
rename(p = `Pr(>|t|)`, str_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, str_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | est | str_err | p | sig |
|---|---|---|---|---|---|
| extraversion | Intercept | 3.921 | 0.027 | 0.000 | *** |
| extraversion | time | -0.029 | 0.024 | 0.233 | |
| extraversion | group | -0.029 | 0.037 | 0.428 | |
| extraversion | time*group | 0.004 | 0.034 | 0.918 | |
| agreeableness | Intercept | 4.020 | 0.030 | 0.000 | *** |
| agreeableness | time | -0.011 | 0.025 | 0.662 | |
| agreeableness | group | -0.001 | 0.042 | 0.980 | |
| agreeableness | time*group | 0.051 | 0.034 | 0.137 | |
| conscientiousness | Intercept | 4.392 | 0.026 | 0.000 | *** |
| conscientiousness | time | 0.026 | 0.024 | 0.270 | |
| conscientiousness | group | -0.001 | 0.036 | 0.977 | |
| conscientiousness | time*group | -0.007 | 0.033 | 0.832 | |
| neuroticism | Intercept | 1.749 | 0.027 | 0.000 | *** |
| neuroticism | time | 0.010 | 0.025 | 0.696 | |
| neuroticism | group | 0.019 | 0.037 | 0.602 | |
| neuroticism | time*group | -0.053 | 0.035 | 0.127 | |
| openness | Intercept | 4.104 | 0.029 | 0.000 | *** |
| openness | time | 0.000 | 0.024 | 0.996 | |
| openness | group | -0.058 | 0.040 | 0.149 | |
| openness | time*group | 0.009 | 0.034 | 0.781 | |
| sociability | Intercept | 3.790 | 0.038 | 0.000 | *** |
| sociability | time | -0.086 | 0.033 | 0.009 | ** |
| sociability | group | -0.071 | 0.053 | 0.178 | |
| sociability | time*group | 0.045 | 0.046 | 0.336 | |
| assertiveness | Intercept | 3.843 | 0.037 | 0.000 | *** |
| assertiveness | time | -0.057 | 0.036 | 0.115 | |
| assertiveness | group | -0.026 | 0.051 | 0.605 | |
| assertiveness | time*group | 0.000 | 0.050 | 0.995 | |
| energy | Intercept | 4.130 | 0.034 | 0.000 | *** |
| energy | time | 0.058 | 0.038 | 0.129 | |
| energy | group | 0.008 | 0.047 | 0.866 | |
| energy | time*group | -0.033 | 0.053 | 0.539 | |
| compassion | Intercept | 3.958 | 0.039 | 0.000 | *** |
| compassion | time | -0.002 | 0.039 | 0.955 | |
| compassion | group | -0.024 | 0.054 | 0.656 | |
| compassion | time*group | 0.043 | 0.054 | 0.428 | |
| respectfulness | Intercept | 4.369 | 0.032 | 0.000 | *** |
| respectfulness | time | 0.002 | 0.030 | 0.959 | |
| respectfulness | group | 0.004 | 0.045 | 0.930 | |
| respectfulness | time*group | 0.035 | 0.042 | 0.399 | |
| trust | Intercept | 3.733 | 0.039 | 0.000 | *** |
| trust | time | -0.030 | 0.036 | 0.407 | |
| trust | group | 0.014 | 0.055 | 0.802 | |
| trust | time*group | 0.077 | 0.050 | 0.127 | |
| organization | Intercept | 4.408 | 0.031 | 0.000 | *** |
| organization | time | 0.008 | 0.032 | 0.796 | |
| organization | group | -0.013 | 0.043 | 0.755 | |
| organization | time*group | 0.051 | 0.045 | 0.253 | |
| productiveness | Intercept | 4.514 | 0.030 | 0.000 | *** |
| productiveness | time | 0.040 | 0.031 | 0.194 | |
| productiveness | group | -0.011 | 0.041 | 0.784 | |
| productiveness | time*group | -0.018 | 0.043 | 0.680 | |
| responsibility | Intercept | 4.255 | 0.034 | 0.000 | *** |
| responsibility | time | 0.032 | 0.032 | 0.311 | |
| responsibility | group | 0.021 | 0.047 | 0.660 | |
| responsibility | time*group | -0.054 | 0.044 | 0.220 | |
| anxiety | Intercept | 1.942 | 0.035 | 0.000 | *** |
| anxiety | time | 0.028 | 0.040 | 0.485 | |
| anxiety | group | 0.055 | 0.049 | 0.261 | |
| anxiety | time*group | -0.118 | 0.056 | 0.034 | * |
| depression | Intercept | 1.528 | 0.029 | 0.000 | *** |
| depression | time | -0.027 | 0.031 | 0.378 | |
| depression | group | 0.014 | 0.041 | 0.736 | |
| depression | time*group | -0.002 | 0.043 | 0.967 | |
| volatility | Intercept | 1.777 | 0.033 | 0.000 | *** |
| volatility | time | 0.030 | 0.032 | 0.357 | |
| volatility | group | -0.010 | 0.046 | 0.826 | |
| volatility | time*group | -0.044 | 0.045 | 0.337 | |
| curiosity | Intercept | 4.092 | 0.036 | 0.000 | *** |
| curiosity | time | 0.016 | 0.034 | 0.634 | |
| curiosity | group | -0.065 | 0.051 | 0.199 | |
| curiosity | time*group | -0.015 | 0.047 | 0.757 | |
| aesthetic | Intercept | 3.817 | 0.039 | 0.000 | *** |
| aesthetic | time | 0.044 | 0.036 | 0.224 | |
| aesthetic | group | -0.011 | 0.054 | 0.834 | |
| aesthetic | time*group | -0.070 | 0.051 | 0.171 | |
| imagination | Intercept | 4.404 | 0.034 | 0.000 | *** |
| imagination | time | -0.058 | 0.036 | 0.108 | |
| imagination | group | -0.096 | 0.048 | 0.044 | * |
| imagination | time*group | 0.113 | 0.050 | 0.024 | * |
Generate word clouds based on the content from the free-form change descriptions written after the intervention at T2.
Prepare data, separately for each intervention group
# see tutorial here: https://towardsdatascience.com/create-a-word-cloud-with-r-bde3e7422e8a
# self-improvement group
# Create a vector containing only the text
text_sb <- df_sbsa %>% filter(!is.na(sb14_01)) %>% pull(sb14_01)
text_sb <- gsub("[\r\n]", " ", text_sb)
# Create a corpus
docs_sb <- tm::VCorpus(tm::VectorSource(text_sb))
# cleaning the text
docs_sb <- docs_sb %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
docs_sb <- tm_map(docs_sb, content_transformer(tolower))
docs_sb <- tm_map(docs_sb, removeWords, stopwords("english"))
# Create a document-term-matrix
dtm_sb <- TermDocumentMatrix(docs_sb)
matrix_sb <- as.matrix(dtm_sb)
words_sb <- sort(rowSums(matrix_sb), decreasing=TRUE)
df_words_sb <- data.frame(word = names(words_sb), freq=words_sb)
# remove a few of the frequent "non-words"
df_words_sb <- df_words_sb %>% as_tibble() %>%
filter(!word %in% c("ive", "didnt", "cant", "dont"))
# self-acceptance group
# Create a vector containing only the text
text_sa <- df_sbsa %>% filter(!is.na(sa13_01)) %>% pull(sa13_01)
text_sa <- gsub("[\r\n]", " ", text_sa)
# Create a corpus
docs_sa <- tm::VCorpus(tm::VectorSource(text_sa))
# cleaning the text
docs_sa <- docs_sa %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
docs_sa <- tm_map(docs_sa, content_transformer(tolower))
docs_sa <- tm_map(docs_sa, removeWords, stopwords("english"))
# Create a document-term-matrix
dtm_sa <- TermDocumentMatrix(docs_sa)
matrix_sa <- as.matrix(dtm_sa)
words_sa <- sort(rowSums(matrix_sa), decreasing=TRUE)
df_words_sa <- data.frame(word = names(words_sa), freq=words_sa)
# remove a few of the frequent "non-words"
df_words_sa <- df_words_sa %>% as_tibble() %>%
filter(!word %in% c("ive", "didnt", "cant", "dont"))Generate the word cloud: for self-improvement group
set.seed(1234) # for reproducibility
wordcloud(words = df_words_sb$word, freq = df_words_sb$freq, min.freq = 1,
max.words=150, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
Generate the word cloud: for self-acceptance group
set.seed(1234) # for reproducibility
wordcloud(words = df_words_sa$word, freq = df_words_sa$freq, min.freq = 1,
max.words=150, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
Lastly, delete free-form text variables again from data set
df_sbsa <- df_sbsa %>% select(-c(sb14_01, sa13_01))
base::save(df_sbsa, file = "data/df_sbsa.rda")